China has played an active and constructive role in the global combat against climate change. Municipalities play a leading role in tackling global climate change. However, despite the global efforts to push for carbon neutrality, the pathway is not yet clear. Using the municipality of Bengbu as a case, this paper explores pathways for achieving carbon emission peak and carbon neutrality in order to reach a CO2 emission peak before 2030 in four Scenarios and develops a low emission framework taking into consideration the local economy, population, renewable resources, as well as the transport and energy sectors based on the Low Emissions Analysis Platform (LEAP) model. Meanwhile, the energy demand, the structure of the electric power generation, the CO2 emission, and the total cost in different Scenarios are simulated respectively. The result of this study provides insight on options for energy transformation for the period of 2020–2060. The results show that the industrial sector is the largest energy consumer. The growth rate of energy demand in the primary industry and industrial sector shows downward trends. The increasement of industrial electrification rate and coal generation phasing out are crucial to CO2 emission reduction. The large-scale deployment of renewable energy and the deployment of thermal power generation with CCUS are important for the electric power industry to achieve carbon reduction goals. Last but not the least, recommendations on energy planning for municipals to realize carbon emission peak and achieve carbon neutrality are provided.

光伏发电是推动我国建设新型能源体系和实现碳达峰碳中和目标的重要抓手之一,同时"双碳"目标对光伏产品的碳减排工作提出了更高的要求.本文通过梳理国内外光伏产品碳排放核算体系思路,基于全生命周期提出了一种产品多层次碳足迹核算方法,并针对我国光伏产品在碳足迹的核算方法不统一、基础数据库建设及更新相对滞后、碳核算管理缺乏系统性及碳足迹规则制定国际话语权不足等挑战,从健全产品碳足迹核算标准体系、搭建全产业链碳排放数据平台等方面出发,提出针对性的政策建议,助力我国光伏产业绿色高质量发展.

China has continually reduced the intensity of its carbon emissions, increased its efforts to fulfil its Nationally Determined Contributions, and boosted its efforts to mitigate climate change. Due to its vast power generation sector, China is at present the world's top carbon dioxide emitter (CO2). Utilizing the Low-Emissions Analysis Platform (LEAP) - which is an integrated, scenario-based energy and environmental modelling tool created by the Stockholm Environment Agency - four scenarios other than the baseline scenario were devised and compared. The current study builds on two Nationally Determined Contributions (NDC) scenarios and two Carbon Neutral (CNT) scenarios in which emissions peak in the year 2025 or 2030, allowing for an examination of ambitious actions necessary beyond business as usual and existing policy trajectories to attain net-zero emissions. This study also looked at how the learning curve affected the expenses in the aforementioned scenarios. It was determined that scenarios that deployed Carbon Capture and Storage (CCS) and Bioenergy with Carbon Capture and Storage (BECCS) technologies were more favourable in realizing China's carbon neutrality goal before 2060 by reaching negative emissions, and scenarios that achieved emissions peak earlier proved higher cost benefits as well. The findings of the study further revealed that the Greenhouse Gas (GHG) savings in the NDC 2025, NDC 2030, CNT 2025, and CNT 2030 scenarios will be 104.23 Gt, 76.77 Gt, 142.74 Gt, and 130.92 Gt respectively, in the study period 2020-2060 and the cost-benefit associated with them per tonne of CO2 will be 8.4, 8.5, 26.4, 30.4 CNY/t CO2, respectively. Moreover, under the CNT 2025 scenario, annual installed capacity of wind power should be greater than 46.8 GW between 2025 and 2030, and greater than 55.2 GW between 2030 and 2060; while the annual installed capacity of solar PV should be greater than 59.2 GW between 2025 and 2030, and greater than 61.3 GW between 2030 and 2060. The Chinese power production industry must seek to convert to a larger-scale deployment of carbon capture technologies such as CCS and BECCS.

能源低碳转型是实现碳达峰、碳中和的关键,关系到我国经济社会发展全局.基于LEAP能源系统模型,以电力行业为重点减排行业,提出中国中长期"双碳"发展路径构想,模拟多情景下的能源需求、能源供给、CO2 排放量和成本,分析能源配置的生态及经济影响.研究发现能源消费呈现"减煤稳油增气,电能替代加速"的局面,终端能源消费可在2040年前达峰,终端能源消费CO2 排放可在2030年前达峰,碳捕集利用与封存(CCUS)技术是实现CO2减排,同时保持一定火电规模以维持电网安全稳定运行的重要手段,且未来逐步具有技术优势.最后,提出了持续推进碳排放总量和强度"双控",以技术革新促进电力系统低碳转型,以及完善全国碳市场建设促进碳排放交易等三方面实现"双碳"目标的政策建议.

Global climate change is a growing concern for the international community. China has played an active and constructive role in the global combat against climate change. Given the dominating role of coal in China’s power supply mix, it is especially urgent to find pathways for the electric power industry to achieve carbon peaking and carbon neutrality. Using the case of a state-owned power generation enterprise, this paper explores pathways for the Enterprise to reach carbon emissions peak and carbon neutrality in five scenarios based on the Low Emission Analysis Platform (LEAP) model. The modeling process takes into consideration of technologies’ learning curve of generation technologies, carbon capture rate, and environmental cost (carbon price) for fossil fuel generators. The LEAP model simulates the structure of the electric power supply, CO2 emissions from power generation, total costs (including capital cost, O&M cost, fuel cost and carbon price), and carbon capture costs. The results show that carbon capture, utilization and storage (CCUS) is crucial to achieving CO2 reduction while maintaining a certain amount of thermal power installed capacity to ensure grid system inertia and security. Biomass power coupled with CCUS is an important carbon negative technology for achieving carbon neutrality. Based on simulations and scenario analysis, this paper proposes policy recommendations for the electric power industry to realize carbon emissions peaking and carbon neutrality.

Renewable energy is emphasized globally due to its potential to contribute to economy and energy sustainable development, as well as mitigate the climate change. Developed and developing countries have set their sights on renewable energy as increasing exhaustion of the fossil energy and deterioration of environmental problem. This article focuses on concise summary and statistic of renewable energy resources potentials, including solar energy, wind energy, bioenergy, geothermal energy, and hydropower. Meanwhile, it provides the renewable energy development status of G8(US, UK, France, Germany, Italy, Canada, Japan, Russia) and BRICS (Brazil, Russia, India, China, and South Africa) countries. The result indicates that renewable energy resources are abundant, especially in China, the US and Russia. Each country has its own resources advantage. China has abundant renewable energy resources but still needs to accelerate renewable energy technology innovation. At last, suggestions are proposed for policy makers on renewable energy penetration.

Under the background of China’s power system reform, the electric power enterprise is required to upgrade its service and optimize the allocation of resources based on the electricity demand. Market competition requires electric power enterprises to pay more attention tothe demandside, and predict the electricity demand, in order to provide timely feedback to system. The electricity consumption of household is mainly predicted by household appliances. The electricity consumption of industrial, agricultural and transportation industries are mainly predicted by the output and mileage of operation. This article uses LEAP (Low Emissions Analysis Platform) to set up a model to simulate the electricity demand in Beijing. This research can provide the significant support for improving the intelligent service of electric power enterprises, and provide a new perspective for electricity demand forecasting.

Stabilization and reduction of the atmospheric carbon dioxide (CO2) concentration will be one of the prime challenges for the energy sector in the upcoming decades. Carbon capture and storage (CCS) is widely seen as a possibility to continue fossil power generation while contributing to CO2 abatement. In future energy systems with high shares of fluctuating renewable energy generation, nuclear power will become increasingly important for power generation. The development of natural gas power is considered as one of effective ways to reduce emissions and socio-economic costs as well. As a measure to establish a climate-friendly energy system, a power system research on coordinated development of nuclear and natural gas power would be considered. Therefore, future electrical power scenarios aimed at environmental and economic effects for China would considered a full mix of energy options. Based on the LEAP tool, this paper establishes a bottom-up model (LEAP-China-Power) to simulate different electric power planning policy scenarios that could be enacted from 2012 to 2050. In addition to a baseline scenario, we design CCS scenario and nuclear and Natural Gas Combined Cycle(N&N) scenario. The results indicate that N&N scenario is superior to CCS scenario. And China’s future power planning recommendations are proposed.

2014年,我国全社会用电量、发电量增速较2013年呈下降趋势,发电设备年利用小时数较2013年大幅下降,为1978年以来最低值.我国经济增速趋缓,经济发展进入新常态.从中长期来看,我国电力需求将放缓,电力过剩时代即将到来.核电作为非化石能源中的主力能源,未来战略地位更加重要.核电走出去面临窗口期,应抓住“一路一带”战略契机,推动核电走出去.本文首先采用LEAP模型,考虑人口增长速度、城市化率、收入水平、经济发展速度以及经济结构等众多因素影响,预测了2015-2050年的电力需求量、电源结构、CO2排放量.进而分析了影响新能源电力发展的原因和国内核电厂址现状,为我国核电加快走出去步伐提供数据支撑和政策参考.

科研单位作为高精尖人才的聚集地,拥有深厚的文化底蕴和技术实力,为科学精神的培养提供了肥沃土壤.在科研管理工作中,注重感性结合理性,理论结合实际,逻辑兼顾直觉.激发科研人员的潜力,增强科研人员的积极性和创造力,赋予科研活动和管理活动生机与活力.形成以科学精神为核心的求实、理性、开放的企业文化,使科研单位的管理水平、创新能力、市场竞争力得到有效提升.

The coal-fired power structure in China will transform to clean and efficient energy power. Based on the long-range energy alternatives planning system, this article establishes a LEAP-China-Electricity model to simulate different electric power planning policy scenarios that could be enacted from 2010 to 2050. In addition to a baseline scenario, carbon capture and storage and nuclear priority scenarios are designed. CO2 emissions and total costs are compared, and the environmental and economic impacts of different scenarios are further analyzed. The sensitivity analysis and environmental assessment can provide useful planning recommendations for China's future power development.

The electric power industry is a large energy production sector,energy consumer and source of greenhouse gases.Here,new and renewable energy resources for countries were defined and reorganized according to various new and renewable energies.Solar,wind,hydro,geothermal,biomass and nuclear energy were examined for G8 and BRIC countries from 2000 to 2008.A new and renewable electric power generation performance index(REPPI) is calculated,and shows that the REPPI of developed countries is generally higher than that of developing countries,but REPPI growth rates in developed countries are lower.The REPPI of countries abundant in new and renewable energy resources is generally lower than that of countries with fewer new and renewable energy sources.The relationship between REPPI and macroeconomic conditions,technological progress,electricity consumption and research and development investment were analyzed using a panel data model.According to data availability and previous studies,per capital GDP,patents,electricity consumption,and new and renewable energy research investment ratios were used.We conclude that technological progress and the new and renewable energy research and development ratio have significant effects on promoting and improving REPPI.However,the effect of per capita GDP on REPPI of some countries has been minimal in recent years,indicating that the REPPI of those countries is not simply determined by economic development.In China,performance of new and renewable energy electric power generation is mainly impacted by per capita GDP and electricity consumption.Policy recommendations for promoting the new and renewable energy electric power development are discussed.

This paper proposes a fuzzy TOPSIS method. An example including an application to distribution center location selection is investigated using the method to illustrate its applications and the differences from the traditional TOPSIS method. The traditional TOPSIS is only suitable for decision making under certain multi-criteria, which requires the decision-making criteria and the attribute entropy of the programs be certain. This study thus proposes fuzzy TOPSIS, which not only is well suited for evaluating fuzziness and uncertainty problems, but also can provide more objective and accurate criterion weights. For solving distribution center location selection problems by using objective and subjective attributes under group decision-making (GDM) conditions, the proposed system integrates fuzzy set theory (FST), the factor rating system (FRS) and simple additive weighting (SAW) to evaluate facility locations alternatives. The FSAWS is applied to deal with both qualitative and quantitative dimensions. The FSAWS process considers the importance of each decision-maker, and the total scores for alternative locations are then derived by homo/heterogeneous group of decision-makers. It is shown that the proposed fuzzy TOPSIS method performs better than other methods.

近年来,电力工程造价上涨较快,控制工程造价的问题显得尤为突出.电力工程造价水平的高低已成为影响电力工业健康发展的一个关键因素.有效地控制电力工程造价,可以充分调动投资者的积极性,同时也是电力企业加强管理、提高效益和竞争力的需要.本文对电力工程造价管理的必要性进行了分析,并提出了电力工程造价管理与控制的措施.

Kinetics of synthesis of bis-(benzoxazolyi-2-methyl) sulfide (BBMS) is investigated under phase-transfer catalysis conditions. Thus, the reaction of 2-chloromethylbenzoxazole and sodium sulfide is carried out in a two-phase (organic/water) medium, and quaternary ammonium salt and quaternary phosphonium salt are used as phase-transfer catalyst (PTC) in the reaction. The conversion of 2-chloromethylbenzoxazole is dramatically enhanced by adding a small quantity of PTC and is also greatly affected by the reaction conditions. The effects of various reaction variables on the kinetics are investigated, including the amount of catalyst, the temperature, the kinds of catalysts, the kinds of solvents, and the agitation speed. An interfacial reaction mechanism is proposed to explain the characteristics of the reaction. A pseudo-first-order rate model is established to describe the relationship between the fractional conversion and the reaction time. The kinetics data demonstrate that the model is suitable to the reaction of synthesis of BBMS. (C) 2009 Wiley Periodicals, Inc. Int J Chem Kinet 41: 296-302, 2009

财务基准收益率是投资项目财务评价指标计算中的重要评判参数.在分析基准收益率在项目财务评价中的重要性的同时,具体分析了影响基准收益率的几种因素,最后提出确定基准收益率确定的2种方法.

Knowledge management is an increasingly important source of competitive advantage for organizations. Knowledge embedded in the organization's business processes and the employee's skills provides the firm with unique capabilities to deliver customers with a product or service. Meanwhile, with the rise of business-to-customers (B2C) E-commerce in recent years, demand for high quality of E-commerce website service has increased. Customer knowledge has been increasingly recognized as a key strategic knowledge resource in organizations. This paper addresses these problems by researching knowledge management in e-commerce and identifying strategies that are currently in use. We will demonstrate how companies can benefit by adopting strategies that harness the potential of knowledge management technologies to transform their e-business activities. We define customer knowledge management as well as provide an overview of it in e-commerce. Finally, we suggest some strategies that can overcome the losing customer problem and limitations in systems presently in use as well as implications for future knowledge management development.

Construction Project Cost Forecasting is a key procedure to the mangement project. An accurate forecast can support the investment decision and ensure the project's feasible at the minimal cost. So reasonable determining and controlling the project cost become the most important task in the budget management of the construction project. A novel regression technique, called Support Vector Machines (SVM), based on the statistical learning theory is exploded in this paper for the prediction of construction project cost. SVM is based on the principle of Structure Risk Minimization as opposed to the principle of Empirical Risk Minimization supported by conventional regression techniques. Through introduced the theory of the SVM-Regression, considered and extracted substances components of construction project as parameters, this paper seted up the Model of the Construction Project Cost Forecasting based on the SVM. The research results show that the prediction accuracy of SVM-Regression is better than that of neural network.

With the development of power markets, forecasting is becoming more and more important in such new competitive markets since the electricity demand forecasting is the basis of decision making for participants in electricity market. The aim of this project is to develop an electricity demand predictor. In this paper, we present an Grey-based prediction algorithm to forecast a long-term electric power demand for the demand-control of electricity. We adopted Grey prediction as a forecasting means because of its fast calculation with as few as four data inputs needed. However, our preliminary study shows that the general Grey model, GM(1,1) is inadequate to handle a volatile electrical system. The general GM(1,1) prediction generates the dilemmas of dissipation and overshoots. Based on these influential factors, the corresponding RBF neutal network forecasting model is presented. The proposed algorithm is more robust and reliable as compared to traditional approach and neural networks.In this study, the prediction is corrected significantly by applying the RBF neural network The satisfactory results with better generalization capability and lower prediction error can be obtained. The present intelligent Grey-based electric demand- control system is able to provide an instrument to save operation costs for high energy consuming enterprises. In such a way, the wastage of electric consumption can be avoided. That is, it is another achievement of virtual electric power plant.

Traditional evaluation methods of bidding have the defect, which is that the weight is determined merely by the subjective judgments of experts or decision makers. An entropy-weight-based technique for order preference by similarity to ideal solution (TOPSIS) method was proposed to decide the weights. According to the method, the weight coefficients in TOPSIS method are given by means of entropy coefficient. The method has the advantages of combining experts' subjective opinions with objective situations and the combining quantitative analyses with qualitative analyses. The proposed method is applied to evaluate information system integration solutions and numerical illustrations proved the effectiveness of the method.