Effect analysis of carbon trading on Economy-Energy-Environment system and calculation of reasonable carbon price intervals

This paper constructs a 4-lier computable general equilibrium model which includes such modules as modeling carbon emission constraints and carbon trading(CT),and incorporates the cost of carbon emissions into constant elasticity of substitute production function.Under scenario settings under different carbon abatement targets,based on 2007 national social accounting matrix and related statistical data about energy consumption and carbon emission,effects on economic outputs,energy consumption,and carbon abatement are estimated and analyzed at both macro and sector level.By calculating selected novel indicators that compromise between macroeconomic opportunity cost and achievable carbon abatement,reasonable carbon price intervals are given for enhancing the robustness and liquidity of carbon market.Further,by decomposition and share-weighted methods,expected carbon abatement and energy price are measured and analyzed in details.Some results are meaningful for fundamental design of the future carbon market.Given constant energy utilization and carbon abatement technologies at the macro level,the higher the carbon price the more actual carbon abatement;the more gross domestic product loss,the less energy consumption.Accwding to the overall situation estimated for 2007 in China,the advice given is to introduce a carbon abatement target rate(R_c)of-10%,which is helpful to make carbon market stable against unexpected carbon price shocks between[6.9,35]/tC with less economic loss.According to Kaya decomposition,after introduction of carbon pricing,carbon abatement is mainly contributed by the effects of energy intensity(EI)and technical progress.Further,CT may help reduce energy consumption and induce transformation to a low-carbon energy structure.At the sector level,the introduction of CT could induce economic recession in all sectors,especially energy.However,the overall economic structure remains unchanged to some extent.CT will help reduce energy consumption in all sectors,especially energy.Overall utilization costs of the energy composite can be divided in two,market price and carbonrelated costs.Carbon-related costs mainly contribute to variation in the utilization cos of the energy composite;carbon pricing may help non-energy sectors achieve sufficient carbon abatement by pushing up energy utilization cost.However,despite achievable carbon abatement by the energy sector being relatively high,induced by carbon pricing,there is still significant potential for other incentive policies to stimulate further abatement,such as energy resources taxation and transportation fuel taxation,especially in the sectors of coal and transportation.Finally,some advice is proposed in regard to policy decisions and further research.

Analysis and forecast of residential building energy consumption in Chongqing on carbon emissions

Carbon emissions mainly result from energy consumption. Carbon emissions inevitably will increase to some extent with economic expansion and rising energy consumption. We introduce a gray theory of quantitative analysis of the energy consumption of residential buildings in Chongqing,China,on the impact of carbon emission factors. Three impacts are analyzed,namely per capita residential housing area,domestic water consumption and the rate of air conditioner ownership per 100 urban households. The gray prediction model established using the Chongqing carbon emission-residential building energy consumption forecast model is sufficiently accurate to achieve a measure of feasibility and applicability.

Influencing factors and contribution analysis of CO_(2) emissions originating from final energy consumption in Sichuan Province,China

Within the context of CO_(2)emission peaking and carbon neutrality,the study of CO_(2)emissions at the provincial level is few.Sichuan Province in China has not only superior clean energy resources endowment but also great potential for the reduction of CO_(2)emissions.Therefore,using logarithmic mean Divisia index(LMDI)model to analysis the influence degree of different influencing factors on CO_(2)emissions from final energy consumption in Sichuan Province,so as to formulate corresponding emission reduction countermeasures from different paths according to the influencing factors.Based on the data of final energy consumption in Sichuan Province from 2010 to 2019,we calculated CO_(2)emission by the indirect emission calculation method.The influencing factors of CO_(2)emissions originating from final energy consumption in Sichuan Province were decomposed into population size,economic development,industrial structure,energy consumption intensity,and energy consumption structure by the Kaya-logarithmic mean Divisia index(LMDI)decomposition model.At the same time,grey correlation analysis was used to identify the correlation between CO_(2)emissions originating from final energy consumption and the influencing factors in Sichuan Province.The results showed that population size,economic development and energy consumption structure have positive contributions to CO_(2)emissions from final energy consumption in Sichuan Province,and economic development has a significant contribution to CO_(2)emissions from final energy consumption,with a contribution rate of 519.11%.The industrial structure and energy consumption intensity have negative contributions to CO_(2)emissions in Sichuan Province,and both of them have significant contributions,among which the contribution rate of energy consumption structure was 325.96%.From the perspective of industrial structure,secondary industry makes significant contributions and will maintain a restraining effect;from the perspective of energy consumption structure,industry sector has a significant contribution.The results of this paper are conducive to the implementation of carbon emission reduction policies in Sichuan Province.

Analysis on Industry Structure Adjustment and Energy Consumption Based on Grey Theory——A Case Study of Qinghai Province

In view of the complexity and non-linearity of energy consumption system and the status quo of the development of energy in Qinghai Province,the relations between energy consumption and industrial structure is analyzed by using the quantitative analysis of grey relation degree by using the grey system theory.The relevancy degree among the primary industry,the secondary industry and the tertiary industry and living energy consumption are obtained,and then the trend of energy consumption in the following several years can be predicted.The results show that the secondary industry has the largest relevancy degree to the total energy consumption.In the end,according to the results of the research,several suggestions on how to saving energy are put forward.Firstly,the government should improve the high-tech industry and restrict the development of high-consumption and high-pollution industries.Secondly,the government should promote the low-carbon way of life;promote energy saving and control the energy consumption of the department of life.Thirdly,clean production should be actively promoted in the tertiary industry and the circular economy should be vigorously expanded.

Cloud-Model-Based Feature Engineering to Analyze the Energy-Water Nexus of a Full-Scale Wastewater Treatment Plant

Wastewater treatment plants(WWTPs)are important and energy-intensive municipal infrastructures.High energy consumption and relatively low operating performance are major challenges from the perspective of carbon neutrality.However,water-energy nexus analysis and models for WWTPs have rarely been reported to date.In this study,a cloud-model-based energy consumption analysis(CMECA)of a WWTP was conducted to explore the relationship between influent and energy consumption by clustering its influent’s parameters.The principal component analysis(PCA)and K-means clustering were applied to classify the influent condition using water quality and volume data.The energy consumption of the WWTP is divided into five standard evaluation levels,and its cloud digital characteristics(CDCs)were extracted according to bilateral constraints and golden ratio methods.Our results showed that the energy consumption distribution gradually dispersed and deviated from the Gaussian distribution with decreased water concentration and quantity.The days with high energy efficiency were extracted via the clustering method from the influent category of excessive energy consumption,represented by a compact-type energy consumption distribution curve to identify the influent conditions that affect the steady distribution of energy consumption.The local WWTP has high energy consumption with 0.3613 kW·h·m^(-3)despite low influent concentration and volumes,across four consumption levels from low(I)to relatively high(IV),showing an unsatisfactory operation and management level.The average oxygenation capacity,internal reflux ratio,and external reflux ratio during high energy efficiency days recognized by further clustering were obtained(0.2924-0.3703 kg O_(2)·m^(-3),1.9576-2.4787,and 0.6603-0.8361,respectively),which could be used as a guide for the days with low energy efficiency.Consequently,this study offers a water-energy nexus analysis method to identify influent conditions with operational management anomalies and can be used as an empirical reference for the optimized operation of WWTPs.

Review of energy consumption research for papermaking industry based on life cycle analysis

Papermaking industry is a high-energy-consuming industry with long supply chain.The growth of paper product demand further intensifies the need of energy consumption.Energy saving through the full supply chain has become a focal point for long-term sustainable development of the papermaking industry.This paper reviews the advances in life cycle analysis for the papermaking industry in recent years.All the stages from the full supply chain are involved to give a panoramic overview of the papermaking industry.The object of this paper is to provide scientific basis to industry and decision-makers with profound understanding of the energy consumption and energy saving potential in a life cycle perspective.

A review of China's energy consumption structure and outlook based on a long-range energy alternatives modeling tool

China＇s energy consumption experienced rapid growth over the past three decades, raising great concerns for the future adjustment of China＇s energy consumption structure. This paper first presents the historical evidence on China＇s energy consumption by the fuel types and sectors. Then, by establishing a bottom-up accounting framework and using long-range energy alternatives plan- ning energy modeling tool, the future of China＇s energy consumption structure under three scenarios is forecast. According to the estimates, China＇s total energy con- sumption will increase from 3014 million tonnes oil equivalent （Mtoe） in 2015 to 4470 Mtoe in 2040 under the current policies scenario, 4040 Mtoe in 2040 under the moderate policies scenario and 3320 Mtoe in 2040 under the strong policies scenario, respectively, lower than those of the IEA＇s estimations. In addition, the clean fuels （gas, nuclear and renewables） could be an effective alternative to the conventional fossil fuels （coal and oil） and offer much more potential. Furthermore, the industry sector has much strong reduction potentials than the other sectors. Finally, this paper suggests that the Chinese government should incorporate consideration of adjustment of the energy consumption structure into existing energy policies and measures in the future.

The relationship between energy consumption and economic growth and the development strategy of a low-carbon economy in Kazakhstan

Fossil energy is the material basis of human survival, economic development and social progress. The relationship between energy consumption and economic growth is becoming increasingly close. However, energy consumption is the major source of greenhouse gases, which can significantly affect the balance of the global ecosystem. It has become the common goal of countries worldwide to address climate change, reduce carbon dioxide emissions, and implement sustainable development strategies. In this study, we applied an approximate relationship analysis, a decoupling relationship analysis, and a trend analysis to explore the relationship between energy consumption and economic growth using data from Kazakhstan for the period of 1993-2010. The results demonstrated： （1） the total energy consumption and GDP in Kazakhstan showed a ＂U＂-type curve from 1993 to 2010. This curve was observed because 1993-1999 was a period during which Kazakhstan transitioned from a republic to an independent country and experienced a difficult transition from a planned to a market economy. Then, the economic system became more stable and the industrial production increased rapidly because of the effective financial, monetary and industrial policy support from 2000 to 2010. （2） The relationships between energy con- sumption and carbon emissions, economic growth and energy exports were linked; the carbon emissions were mainly derived from energy consumption, and the dependence of economic growth on energy exports gradually increased from 1993 to 2010. Before 2000, the relationship between energy consumption and economic growth was in a recessional decoupling state because of the economic recession. After 2000, this relationship was in strong and weak decoupling states because the international crude oil prices rose and energy exports increased greatly year by year. （3） It is forecasted that Kazakhstan cannot achieve its goal of energy consumption by 2020. Therefore, a low-carbon economy is the best strategic choice to address climate change from a global perspective in Kazakhstan. Thus, we proposed strategies including the improvement of the energy consumption structure, the development of new energy and renewable energy, the use of cleaner production technologies, the adjustment and optimization of the industrial structure, and the expansion of forest areas.

A process-level hierarchical structural decomposition analysis (SDA) of energy consumption in an integrated steel plant

A hierarchical structural decomposition analysis(SDA) model has been developed based on process-level input-output(I-O) tables to analyze the drivers of energy consumption changes in an integrated steel plant during 2011-2013. By combining the principle of hierarchical decomposition into D&L method, a hierarchical decomposition model for multilevel SDA is obtained. The developed hierarchical IO-SDA model would provide consistent results and need less computation effort compared with the traditional SDA model. The decomposition results of the steel plant suggest that the technology improvement and reduced steel final demand are two major reasons for declined total energy consumption. The technical improvements of blast furnaces, basic oxygen furnaces, the power plant and the by-products utilization level have contributed mostly in reducing energy consumption. A major retrofit of ancillary process units and solving fuel substitution problem in the sinter plant and blast furnace are important for further energy saving. Besides the empirical results, this work also discussed that why and how hierarchical SDA can be applied in a process-level decomposition analysis of aggregated indicators.

Sensitivity Analysis of Energy Consumption for Cylinder Efficiency of A 1 000 MW Steam Turbine Unit

以东汽1000MW汽轮机为研究对象，采用汽轮机组定功率变工况计算方法，对汽轮机各汽缸效率在THA、70％THA、50％THA、40％THA滑压工况进行敏度分析，得到相应工况下各汽缸效率变化对汽轮机热耗率和机组发、供电煤耗率的影响规律。分析表明，缸效率的能耗敏度随缸效率变化基本呈线性关系；低压缸效率的能耗敏度最大，高压缸效率次之，中压缸效率较小；随机组负荷降低，高压缸效率的能耗敏度增加，中、低压缸效率的能耗敏度变化较小。在THA工况下低压缸效率下降1％，供电煤耗敏度绝对值增加1．246g／（kW·h），相对值升高0．433％；高压缸效率下降1％，供电煤耗敏度的绝对值增加0．44g／（kW．h），相对值升高0．154％；中压缸效率下降1％，供电煤耗敏度的绝对值增加0．352g／（kW．h），相对值升高0．122％。分析结果可对同类型机组进行节能诊断，进而指导机组的优化运行。

Correlation Analysis of Energy Consumption of Agricultural Rotorcraft

With the rapid development of unmanned aerial vehicle technology,unmanned aerial vehicles(UAVs)have been widely used in the field of agricultural plant protection.Compared with fuel-driven UAVs,electrically driven rotorcrafts have many advantages such as lower cost,simpler operation,good maneuverability and cleaner power,which them popular in the plant protection.However,electrical rotorcrafts still face battery problems in actual operation,which limits its working time and application.Aiming at this issue,this paper studied the influence of rotorcraft flight parameters on energy consumption through series of carefully designed flight experiments.First of all,the linear motion experiments have been designed that the rotorcraft was made to perform speed tests and acceleration test with the speed varied from 2∼9 m/s.Secondly,the turning maneuver experiments are carried out under the different circular routes,a rotorcraft was made to conduct successive steering maneuvers at a certain speed of 2 m/s.With the collected tests data,the relation of the energy consumption and the flight dynamic parameter are analyzed through correlation analysis,and the test results of different pairs of experiments have been compared.The research results of this paper would encourage the agricultural rotorcraft to make less maneuvers during operation,which can also provide practical experience and data support for subsequent optimization of flight parameters and reduction of energy consumption.

Analysis of the Energy Efficiency of Diesel Oil Consumption in the Brazilian Iron Ore Mining Company

This paper aims to evaluate the diesel oil consumption between 2008 and 2015 in the production of iron ore in Brazil, creating correlations between energy intensity (production), economy and checking the impact of fuel prices on the commodity. During the analyzed period, the years 2008-2009 indicated economic crises, which interfered in the price and the commercialization of iron ore products. The physical intensity was 0.2% higher than the economic intensity. In the period from 2010 to 2014, economic activity remained more stable, with a decreasing trend, mainly due to the increase of iron ore prices. The physical intensity is much higher than the economic intensity influenced by the expansion of the Chinese economy. The year of 2014 indicated the end of the high iron ore price cycle and the beginning of supply and demand stabilization with consequent reduction in prices. In 2015, the market entered the stabilization phase, with a continuous reduction in unit production costs and transportation logistics. There was an abrupt change due to the strong increase of the economic intensity due to the fall of the international prices of iron ore. The diesel oil consumption plays a vital role in the scenario of cost reduction in iron ore production and a deeper analysis must be done in order to discover some options to change the energy matrix.

Time Series Analysis of Energy Intensity, Value Added Tax and Corporate Income Tax: A Case Study of the Non-Ferrous Metal Industry, Jiangxi Province, China

Unprecedented industrialization and urbanization have led to China’s poor energy efficiency. In response, the Chinese government has set goals to reduce energy consumption that may include implementing new tax policies. In this paper, we investigate the relationship between energy intensity, an indicator that measures the efficiency of energy consumption, and two sources of government revenue in China (i.e., value-added tax (VAT) and corporate income tax). As a case study, we developed a Granger co-integration model to analyze the dynamic relationship of energy intensity, VAT and corporate income tax in the non-ferrous metal industry, Jiangxi Province, China, between 1996 and 2010. Augmented Dickey-Fuller tests were used to validate the model. In our time series analyses, we found when controlling for corporate income tax, a one log unit increase of VAT resulted in a decrease of 1.17 log units of energy intensity. However, when controlling for VAT, a one log unit increase of corporate income tax resulted in an increase of 0.34 log units of energy intensity. Understanding the relationship between energy intensity and taxation in industries that consume high volumes of energy can greatly enhance China’s goal to reduce energy consumption. We believe our findings add to this on-going discussion.

Optimization of energy consumption structure based on carbon emission reduction target:A case study in Shandong Province,China

It is urgent to significantly reduce greenhouse gas emissions to actively deal with global warming.This paper investigates Shandong Province,a typical province of energy consumption,as the research object,aiming to optimize total energy consumption and consumption structure in the future planning year.This paper constructs a methodological system to optimize energy consumption structure in Shandong Province,using a scenario combination of system dynamics(SD)prediction and analysis based on the coupling of key scenario elements affecting different energy consumption from different perspectives.Structural equation modeling and SD sensitivity analysis indicate an overlap between key factors restricting energy consumption.Pairing the key scenario factors can better reflect the internal mechanism of energy consumption development.Based on this,21 scenarios based on different combinations of the key elements are constructed.Through SD prediction and analysis,the most suitable scenario mode for optimizing energy consumption structure in Shandong Province is selected.This paper provides a suitable development range for the average gross domestic product growth rate,the proportion of secondary industry,energy consumption intensity of secondary industry,and the urbanization rate for Shandong Province.This paper can provide a reference for similar research and the government in formulating the optimization scheme of energy consumption structure.

Cross-Layer Energy Analysis and Proposal of a MAC Protocol for Wireless Sensor Networks Dedicated to Building Monitoring Systems

In a sustainable development context, the monitoring systems are essential to study the building energy performances. With the recent technology advances, these systems can be based on wireless sensor networks, where the energy efficiency is the main design challenge. To this end, most of the studies focus on low power Medium Access Control (MAC) protocols to reduce the overall energy consumption of a network. Nevertheless, the performances assessment of these protocols is generally not performed in a realistic way, and does not take into account the performances of the other layers of the OSI model. In this paper, we propose a cross-layer methodology to assess the real performances of a MAC protocol by taking into account the traffic volume, the synchronization losses and more particularly the physical layer performances through a Bit Error Rate (BER) criterion. The simulation results demonstrate clearly the physical layer impact on a sensor lifetime. Finally, the proposal of an energy efficient MAC protocol for a wireless sensor network dedicated to an application of building monitoring is proposed.

Calculating Method for Influence of Material Flow on Energy Consumption in Steel Manufacturing Process

From the viewpoint of systems energy conservation, the influences of material flow on its energy consumption in a steel manufacturing process is an important subject. The quantitative analysis of the relationship between material flow and the energy intensity is useful to save energy in steel industry. Based on the concept of standard material flow diagram, all possible situations of ferric material flow in steel manufacturing process are analyzed. The expressions of the influence of material flow deviated from standard material flow diagram on energy consumption are put forward.

Estimation of ground heat flux and its impact on the surface energy budget for a semi-arid grassland

Three approaches, i.e., the harmonic analysis （HA） technique, the thermal diffusion equation and correction （TDEC） method, and the calorimetric method used to estimate ground heat flux, are evaluated by using observations from the Semi-Arid Climate and Environment Observatory of Lanzhou University （SACOL） in July, 2008. The calorimetric method, which involves soil heat flux measurement with an HFP01SC self-calibrating heat flux plate buried at a depth of 5 cm and heat storage in the soil between the plate and the surface, is here called the ITHP approach. The results show good linear relationships between the soil heat fluxes measured with the HFP01SC heat flux plate and those calculated with the HA technique and the TDEC method, respectively, at a depth of 5 cm. The soil heat fluxes calculated with the latter two methods well follow the phase measured with the HFP01SC heat flux plate. The magnitudes of the soil heat flux calculated with the HA technique and the TDEC method are close to each other, and they are about 2 percent and 6 percent larger than the measured soil heat flux, respectively, which mainly occur during the nighttime. Moreover, the ground heat fluxes calculated with the TDEC method and the HA technique are highly correlated with each other （R2= 0.97）, and their difference is only about 1 percent. The TDEC-calculated ground heat flux also has a good linear relationship with the ITttP-calculated ground heat flux （R2 = 0.99）, but their difference is larger （about 9 percent）. Furthermore, compared to the HFP01SC direct measurements at a depth of 5 cm, the ground heat flux calculated with the HA technique, the TDEC method, and the ITHP approach can improve the surface energy budget closure by about 6 percent, 7 percent, and 6 percent at SACOL site, respectively. Therefore, the contribution of ground heat flux to the surface energy budget is very important for the semi-arid grassland over the Loess Plateau in China. Using turbulent heat fluxes with common corrections, soil heat storage between the surface and the heat flux plate can improve the surface energy budget closure by about 6 to 7 percent, resulting in a closure of 82 to 83 percent at the SACOL site.

Analysis of Renewable Energy Utilization Potential in Buildings of China

As a country of great population, China has increasing building energy consumption continuously. It not only threatens the lack of total energy but also hardens the progress of protecting environment. Therefore, it forces the country to accelerate finding substitution application of conventional energy in building, renewable energy building utilization. In base of 2010, this study explores the potential of the renewable energy building utilization by using energy consumption analysis until 2030 and predicts annual alternative quantity of renewable energy in different situations.

Prediction of Low-Energy Building Energy Consumption Based on Genetic BP Algorithm

Combined with the energy consumption data of individual buildings in the logistics group of Yangtze University,the analysis model scheme of energy consumption of individual buildings in the university is studied by using Back Propagation(BP)neural network to solve nonlinear problems and have the ability of global approximation and generalization.By analyzing the influence of different uses,different building surfaces and different energysaving schemes on the change of building energy consumption,the grey correlation method is used to determine the main influencing factors affecting each building energy consumption,including uses,building surfaces and energy-saving schemes,which are used as the input of the model and the building energy consumption as the output of the model,so as to establish the building energy consumption analysis model based on BP neural network.However,in practical application,BP neural network has the defects of slow convergence and easy to fall into local minima.In view of this,this paper uses genetic algorithm to optimize the weight and threshold of BP neural network,completes the improvement of various building energy consumption analysis models,and realizes the qualitative analysis of building energy consumption.The model verification results show that the viscosity of the building energy consumption analysis model based on genetic algorithm improved BP neural network algorithm(GABP)in this paper is relatively high,which is more accurate than the results of the traditional BP neural network model,and the relative error of the analysis model is reduced from 11.56%to 8.13%,which proves that the GABP can be better suitable for the study of school building energy consumption analysis model,It is applied to the prediction of building energy consumption,which lays a foundation for the realization of carbon neutralization in the South expansion plan of Yangtze University.