鸡内源总能的变异及其对饲料原料真代谢能值的影响

本试验旨在研究试验鸡不同季节、不同批次间内源总能的变异及其对饲料原料真代谢能值的影响。试验采用单因素完全随机设计,分春季、夏季和秋季3个季节共计12个批次测定鸡内源总能及其对饲料原料[玉米、玉米干酒糟及其可溶物（DDGS）、木薯干和木薯渣]真代谢能值的影响,每个批次设4个重复,每个重复3只鸡。将各季节内各批次内源总能的平均值作为该季节内源总能,并根据不同季节的内源总能分别计算其对4种饲料原料真代谢能值的影响。结果表明：1）12个测定批次间48 h内源总能差异显著（P〈0.05）,但3个季节内48 h内源总能差异均不显著（P〉0.05）,因此,可将测定季节内各个批次间48 h内源总能数据合并,将其平均值作为该季48 h内源总能。2）对比3个季节间的48 h内源总能,秋季48 h内源总能（67.97 kJ/只）极显著低于春季（83.07 kJ/只）和夏季（79.90 kJ/只）（P〈0.01）,但春季和夏季间48 h内源总能差异不显著（P〉0.05）。3）季节内48 h内源总能与48 h内源干物质排泄量呈极显著正相关（r≥0.91,P〈0.01）。4）在玉米、玉米DDGS、木薯干及木薯渣4种饲料原料中,不同季节48 h内源总能的最大变化量分别占饲料总能排泄量的7.36%～8.38%、2.68%～2.94%、7.92%～10.86%和3.53%～3.96%,不同季节48 h内源总能的变异引起饲料原料真代谢能值的变化范围为0.28～0.36 kJ/g。由此可见,鸡内源总能在季节间存在一定的变异,但是该变异对饲料原料真代谢能值的计算并无显著影响。

Impacts of Carbon Tax Policy on CO_2 Mitigation and Economic Growth in China

In this paper we examine the impacts of carbon tax policy on CO2 mitigation effects and economic growth in China by using a dynamic energy-environment-economy computable general equilibrium （CGE） model. The results show that 30, 60, and 90 RMB per ton CO2 of carbon tax rate will lead to a reduction of CO2 emissions by 4.52%, 8.59%, and 12.26%, as well as a decline in the GDP by 0.11%, 0.25%, and 0.39% in 2020, respectively, if carbon tax revenues are collected by the government. Moreover, with energy efficiency improvements the CO2 emission per unit of GDP will equally drop by 34.79%, 37.49%, and 39.92% in 2020, respectively. Negative impacts on sectors and households will be alleviated if carbon tax revenues are returned to these sectors and households.

An analysis of China's CO_2 emission peaking target and pathways

China has set the goal for its CO2 emissions to peak around 2030, which is not only a strategic decision coordinating domestic sustainable development and global climate change mitigation but also an overarching target and a key point of action for China＇s resource conservation, environmental protection, shift in economic development patterns, and CO2 emission reduction to avoid climate change. The development stage where China maps out the CO2 emission peak target is earlier than that of the developed countries. It is a necessity that the non-fossil energy supplies be able to meet all the increased energy demand for achieving CO2 emission peaking. Given that China＇s potential GDP annual increasing rate will be more than 4%, and China＇s total energy demand will continue to increase by approximately 1.0%--1.5% annually around 2030, new and renewable energies will need to increase by 6%-8% annually to meet the desired CO2 emission peak. The share of new and renewable energies in China＇s total primary energy supply will be approximately 20% by 2030. At that time, the energy consumption elasticity will decrease to around 0.3, and the annual decrease in the rate of CO2 intensity will also be higher than 4% to ensure the sustained growth of GDE To achieve the CO2 emission peaking target and substantially promote the low-carbon deve!opment transformation, China needs to actively promote an energy production and consumption revolution, the innovation of advanced energy technologies, the reform of the energy regulatory system and pricing mechanism, and especially the construction of a national carbon emission cap and trade system.

Developing energy forecasting model using hybrid artificial intelligence method

An important problem in demand planning for energy consumption is developing an accurate energy forecasting model. In fact, it is not possible to allocate the energy resources in an optimal manner without having accurate demand value. A new energy forecasting model was proposed based on the back-propagation(BP) type neural network and imperialist competitive algorithm. The proposed method offers the advantage of local search ability of BP technique and global search ability of imperialist competitive algorithm. Two types of empirical data regarding the energy demand(gross domestic product(GDP), population, import, export and energy demand) in Turkey from 1979 to 2005 and electricity demand(population, GDP, total revenue from exporting industrial products and electricity consumption) in Thailand from 1986 to 2010 were investigated to demonstrate the applicability and merits of the present method. The performance of the proposed model is found to be better than that of conventional back-propagation neural network with low mean absolute error.

Study on China＇s Economic Vulnerability to Energy Import Using Decomposition Method

Vulnerability means the degree to which that a system is susceptible to suffer damage. This paper focuses on the economic vulnerability to risk of energy import by employing ratio of net energy import to GDP as indicator, and decomposes the vulnerability change into effects of energy import, structure and intensity in order to find out key factors that influence economic security to energy import. Decomposition analysis on China indicates that effect of rising energy import takes more than 90 percent of total vulnerability change during the last 10 years, along with insignificant effect of structural change and intensity decline. International analysis on cross- section data of net energy importers also presents the positive relationship between external energy dependence and economic vulnerability. However, results of America show that long-term effect of energy intensity is much larger than China from 1954 to 2007, which is 70.8% of its total vulnerability change. Experience from developed countries confirms the necessary and validity of improving energy efficiency on depressing economic vulnerability to energy import, which provides lessons for the energy development of China.