Whether do the principal results of the traditional
高中英语语法综合测试100题(含答案)
综合测试❖综合过关测试1.本测试题共100题, 每题1分, 共100分, 试题由某名校老师撰稿, 部分试题内容涉及虚拟语气, 且试题有一定难度, 供参考、选用Mr.Bus.i.o.tim.fo.everything.____t.fo.th.openin.ceremony?2. A.How B.Why C.What D.Whether3._____ the doctors really doubt is _____ my mother will recover from the serious disease soon.4. A.What…that B.What…whether C.That…thatD.That…whether5.Do you think _____ is no possibility _____ Tom can win the first prize in the contest?6. A.there…whether B.there…that C.it…whether D.it…that7.Jean Goodall made up her mind to dedicate all she had to _____ the chimpanzees in the open air.8. A.observe B.havin.observed C.hav.observedD.observing9._____ what you can and many poor people will spend the cold winter warmly.10. A.Donating B.T.donate C.Havin.donatedD.Donate11.You won’t succeed in the end _____ you don’t give up halfway!12. A.eve.though B.a.though C.a.lon.as D.a.fa.as13.Having suffered heart trouble for years, Professor White _____ take some medicine with him_____ he goes.14. A.must…where B.must…wherever C.ha.to…whereD.ha.to…wherever15.He hesitated for a moment _____ he kicked the ball, otherwise he would have scored a goal.16. A.until B.after C.before D.unlessενϕοψετηφοοϖερμυχηI’γλαψολικιτ∏λεασδρο⎽⎽⎽⎽ψολικεψεσωιλλ17. A.eac.time B.ever.time C.i.an.time D.a.an.time18._____ you do, you should put your heart into it.19. A.However B.How C.Whatever D.What20.The rubber plantation extends _____ the river.21. A.a.lon.as B.a.fa.as C.s.fa.as D.a.wel.as22.You might as well figure out some situations _____ you may meet with in your business.23. A.that B.where C.lest D.i.case24.Word that they failed to pass their driving test discouraged their boss, _____?25. A.di.they B.didn’.they C.didn’.it D.di.it26.Under no circumstances _____ in such a meaningless discussion.27. A.di.h.participate B.participate.he C.h.participatedD.h.di.participate28.Recently I bought an ancient Chinese vase, _____ was quite reasonable.29. A.whic.price B.o.whic.th.price C.pric.o.whichD.i.whos.price30.I know nothing about the young lady _____ she came from Beijing.31. A.except B.excep.for C.excep.whatD.excep.when32.To tell you the truth, I am far from _____ with what he has done.33. A.satisfactory B.satisfying C.satisfied D.pleasing34._____ he had lain quietly as the doctor instructed, he would not suffer so much now.35. A.Onl.if B.I.case C.I.only D..wish36.- _____ I help you with that suitcase?- It’s all right, thanks.37. A.Can B.May C.Shall D.Will38.- Do you mind if I keep pets in this building?- I’d _____ you didn’t, actually.39. A.wish B.love C.better D.rather40.We volunteered _____ money to help the victims of the earthquake.41. A.t.collect B.collecting C.havin.collectedD.t.hav.collected42._____ the fact that the working mother is very busy, she still devotes a lot of time to her children.43. A.Regardles.of B.Although C.While D.Thank.to44.Fred is _____ second to none in _____ mathematics in our class, but believe it or not, he hardlypasses the last exam.45. A.the…the B./…/ C.the…/ D./…the46._____ Nobel Prizes are awarded to the scientists who have made great achievements in _____certain field.47. A.The…a B.The…the C./…a D./…the48._____ you get into a bad habit, it’s very difficult to get rid of it.49. A.Once B.Th.instant C.Th.firs.time D.Unless50.This photo reminds me _____ the days we spent in the summer camp.51. A.for B.of C.on D.during52.As we all know, success results from hard work; _____ efforts nothing can be achieved.53. A.excep.for B.i.additio.to C.without D.despite54.The students have come _____ that friendship is more important than money and _____ a friend inneed is a friend indeed.55. A.realizing…that B.realizing…/ C.t.realize…/D.t.realize…that56._____ I know, it is convenient to do shopping in that small city.57. A.S.fa.as B.A.lon.as C.A.wel.as D.A.soo.as58.Going to bed early and getting up early _____ good to your health.59. A.makes B.make C.does D.do60.Sometimes you walk among the huge trees _____ the sunlight filters through.61. A.when B.that C.which D.where62.Just _____ when a friendly pup comes near you, I reached up and scratched the deer’s head, rightbetween the horn.63. A./ B.as C.like D.since64._____ that early winter morning, the hunter has quitted _____ for good.65. A.Since…hunting B.Since…t.hunt C.From…huntingD.From…t.hunt66._____, after high school graduation in 1952, Goodall was working as a secretary at OxfordUniversity, she knew she wanted to go to Africa.67. A.Eve.though B.Eve.when C.Even D.I.thatI.195.sh.wa.invite.t.Keny.t.visi..friend.____.sh.me.th.world-renowne.anthropologis.Loui.S.B.Leakey.68. A.who B.that C.where D.when69._____ the young man had learned five foreign languages attracted the audience’s interest.70. A.What B.How C.If D.Whatever71._____ that he understands what is to be done.72. A.Makin.sure B.T.mak.sure C.Mak.sureD.Havin.made73.They had done what they could to save the drowning child; _____ he came to at last.74. A.yet B.therefore C.furthermoreD.neverthelessMr.Smit.i.sai.t.hav.entere.fo.th.lon.jum.yesterday._____?75. A.isn’.he B.doesn’.he C.hasn’.he D.didn’.he76.I don’t suppose you had one of your teeth pulled out yesterday, _____?77. A.d.I B.ha.you C.di.you D.didn’.you78.I told him what I was happy _____ that he had done his best.79. A.is B.was C.wit.is D.wit.was.I’.lik.t.invit.yo.t.hav.th.Ne.Year’..Than.you.I’._____.80. A.car.to B.like C.love e81.Enough _____ on how to make wise use of your space time to have further education.82. A.ar.said B.i.said C.ha.bee.saidD.hav.bee.said83.The rescue team made every effort _____ the missing mountain climber.84. A.t.locate B.locating C.t.hun.for D.huntin.for85.He _____ there, but he couldn’t find the time.86. A.mus.hav.been B.ough.t.beC.wa.suppose.t.hav.beenD.shoul.be87.– Do you feel like _____ a walk in the park?– Yes, _____ I can’t afford the time.88. A.taking…but B.t.take…but C.taking…howeverD.t.take…however89.How mistaken _____ you to look _____ it as impossible!90. A.for…at B.for…on C.of…at D.of…on91.You don’t know how desperate I am _____.92. A.t.se.of.you B.t.se.yo.in C.seein.of.youD.seein.yo.in93.Nobody regards it as right _____ reading in the room all day.94. A.t.keep B.keeping C.t.b.kept D.bein.kept95._____ Disney’s managers get involved in the daily management of the park.96. A.Even B.Eve.if C.Eve.when D.Eve.whatlions of people pass _____ the gates of Disney’s entertainment parks in California, Florida andJapan each year.98. A.across B.through C.by D.away99.Obvious _____ this may seem, it is almost unbelievable how many people would rather sit in silentignorance _____ admit not understanding.100. A.though…than B.though…of C.although…thanD.although…of 101.I prefer my newly-bought flat _____ it was.102. A.to B.instea.of C.as D.rathe.than103.– Would you like to go to the Grand Theatre with me tonight?– I’d like to, _____ I have an exam tomorrow.104. A.yet B.still C.and D.but105._____ I understand what you say, I can’t agree with you.106. A.While B.As C.When D.Since107.The Ambassador’s wife went on asking questions, _____ she suddenly noticed a big hole in her husband’s hat.108. A.which B.when C.th.moment D.an.time109.As we all know, it was a time _____ motorcars were care.110. A.that B.when C.before D.since111.I can’t tell you _____ you won’t listen.112. A.unless B.until C.when D.but113.It is a good way for us to memorize new words _____ seeing them repeatedly.114. A.on B.at C.in D.by115.Washington, a state in the United States, _____ in honor of one of the greatest American presidents.116. d d d117.Those T-shirts are usually $10 _____, but today they have a special price of $1 in the Shopping Mall.118. A.every B.each C.one D.single119.There are five cakes to choose from, but I’m at a loss _____.120. A.wit.whic.t.buy B.wit.whic.t.b.boughtC.whic.t.buyD.whic.t.b.bought121.She had _____ got to the lab when she set about conducting the experiment.122. A.rather B.scarcely C.nearly D.simply123.– Could you tell me what made you look upset all day?– _____.124. A.Becaus.o.m.missin.MDB.Becaus.m.M.wa.missingC.T.los.m.MDD.M.losin.MD____.wa.muc.fu.a.yesterday’e.bu.wh.____.you?125. A.It…didn’t B.It…hadn’t C.There…didn’tD.There…hadn’t Look.thos.flower.i.th.vas.hav.fade.Yo.migh.a.wel.g.an.bu.anothe._____.126. A.one B.bunch C.series D.species127._____ to help others in need and you will feel on top of the world.128. A.Bein.ready B.I.bein.ready C.T.b.ready D.Ready129.The Olympics, as we all know, _____ a wonderful opening ceremony.130. A.begin.with B.begi.with C.i.begu.withD.ar.begu.with 131._____ has helped to rescue the drowning boy is worth _____.132. A.Whoever…bein.praised B.Whoever…praisingC.Who…bein.praisedD.Who…praising133.When I returned home, I found my son _____ asleep.134. A.quickly B.sound C.wide D.well135.E-mail, in addition to mobile phones, _____ an active part in daily communication.136. A.plays B.play C.takes D.take137.The teacher gave Mary a better offer than _____.138. A.tha.o.Tom’s B.Tom’s C.h.gav.TomD.thos.o.Tom 139._____ your examination papers with great care, some careless spelling mistakes can be avoided.140. A.Havin.gon.over B.G.over C.I.yo.g.over D.T.g.over141.As a matter of fact, most of us _____ hoped to be invited to the party.142. A.lot.of B.kin.of C..grea.dea.of D.to.much143.My brother met her at the Grand Theatre yesterday afternoon, _____ he couldn’t have attended your lecture.144. A.but B.otherwise C.and D.so145._____ a single song did Mary sing at the Christmas Party.146. A.Few B.No C.Never D.Not147.Do you see the building, _____ a policeman is running?148. A.fo.which B.acros.which C.i.whos.directionD.i.it.direction 149.I sent him an e-mail, _____ to gain further information about my study of English.150. A.wishing B.t.wish C.wished C.tha.wishes151.I wish to lend the poor man a hand, _____?152. A.d.I B.don’.I C.doesn’.he D.ma.I153.– Shall I lend you a hand _____ that suitcase?– It’s all right, thanks.154. A.with B.to C.for D.in155.– You _____ her before you left.– I mean to, but when I was leaving I couldn’t find her anywhere.156. A.wer.t.thank B.wa.suppose.t.thankC.ough.t.hav.thankedD.coul.hav.thanked157._____ most students, Mary was always well prepared and never came late _____ class.158. A.As…for B.As…to C.Like…for D.Like…to159.– Do you know whose car they borrowed for the trip?– _____.160. A.Browns’ B.Th.Browns’ C.Th.BrownsD.Th.Brown’s 161.I wonder how long they _____ in such an important scientific experiment.162. A.engaged B.hav.engaged C.ha.engagedD.hav.bee.engaged 163.He did dig a hole _____ an apple.164. A.th.thre.time.siz.of B.thre.time.th.siz.ofrg.asrge.as165.It seems to me that Mary’s hair style is similar to _____.166. A.Jane B.on.o.Jane C.tha.o.Jane D.Jan.is 167._____ worried the boy most was _____ to the university he had been dreaming of.168. A.What…hi.no.bein.admitted B.What…hi.bein.no.admittedC.That…hi.no.bein.admittedD.That…hi.bein.no.admitted169.I fell in love with her _____ I caught sight of her on the stage.170. A.b.th.time B.th.firs.time C.b.th.instantD.fo.th.firs.time 171.On hearing the news that our women volleyball team won the championship in Japan, we all felt _____ happy.172. A.n.mor.than B.les.than C.mor.than D.rathe.than 173.It is such _____ unusual work of art that everyone _____ to have a look at it.174. A.an…wants B.an…ca.want C.a…wantsD.a…ca.want 175.Tom made another significant breakthrough, which I think is _____ great importance _____ science.176. A.as…to B.as…in C.of…to D.of…in 177.We all know it is a matter of _____ will sign up for the event of high jump.178. A.who B.whoever C.whom D.whomever 179.– I enjoy swimming, but I am not going to swim today.– _____.180. A.S.d.I B.No.d.I C.Neithe.a.ID.S.i.i.wit.me Mrs.Brow.wa.muc.delighte.t.se.th.worke.sh.ha.____.he.washin.machin.agai.i.th.street.181. A.ha.repaired B.ha.i.repaired C.ha.repair D.t.b.repaired 182.Don’t take _____ for granted _____ all those who get good grades in the entrance examination will turn out to be most successful.183. A.this…that B.this…what C.it…that D.it…what 184.Linda had no time to chat with me when I last saw her _____ she was hurrying to the office.185. A.that B.until C.as D.although 186.During the SARS, washing hands before meals seems to them a rule never meant _____.187. A.t.break B.breaking C.t.b.brokenD.bein.broken 188.Anyone _____ punishment if he does something wrong.189. A.should B.deserves C.needs D.requires 190.Don’t worry, you _____ get the answer this afternoon.191. A.will B.shall C.ar.goin.to D.could____.yo.se.Mr.Wes.today.Mrs.Brown?A.WillB.ShallC.CanD.May1~10 ABBDD ADCCC 11~20 BACAB DCCCD 21~30 AABAA BCDAC31~40 DBABC BCBDC 41~50 DDCAC ADBCA 51~60 BACDA BBCDC61~70 BCBDC BDDBB 71~80 ACCBD DCADA 81~90 CDBDB CABCA91~100 CADCC CCBBA。
新视野大学英语三课后答案及课文翻译Unit-3
Unit 3Section APre-reading activities一1C 2D 3E 4B 5ALanguage focusWord in use三1integral 2cherish 3afflicted 4noteworthy 5portray 6compliment 7domain 8anonymous 9conscientious 10perpetualWord building四Words learned New words formed-izeGeneral GeneralizeNormal NormalizePublic PublicizeMinimum MinimizeImmune ImmunizeMobile Mobilize-orInvest InvestorDictate DictatorConquer ConquerorInvestigate InvestigatorModerate ModeratorElevator Elevate五1normalize 2moderator 3immunized 4investors 5mobilize 6conqueror 7elevate 8publicizes 9investigator 10minimized 11generalize 12dictatorBanked cloze六1C 2I 3F 4L 5A 6H 7K 8N 9E 10BExpression in use七1embark on 2be deprived of 3turn down 4taken captive 5live on 6share in7was stricken by 8led by exampleTranslation莱奥纳多·达·芬奇是意大利文艺复兴时期最伟大的思想家之一，也许也是迄今最多才多艺的人。
他是画家、雕刻家、建筑家、数学家、工程师和发明家，因成就广泛而闻名。
外文文献翻译——顾客满意度(附原文)
外文文献翻译(附原文)译文一:韩国网上购物者满意度的决定因素摘要这篇文章的目的是确定可能导致韩国各地网上商场顾客满意的因素。
假设客户的积极认知互联网购物的有用性,安全，技术能力,客户支持和商场接口积极影响客户满意度。
这也是推测,满意的顾客成为忠实的客户。
调查结果证实，客户满意度对顾客的忠诚度有显著影响,这表明,当顾客满意服务时会显示出很高的忠诚度.我们还发现,“网上客户有关安全风险的感知交易中，客户支持,网上购物和商场接口与客户满意度呈正相关.概念模型网上购物者可以很容易的将一个商场内的商品通过价格或质量进行排序，并且可以在不同的商场之间比较相同的产品。
网上购物也可以节省时间和降低信息搜索成本。
因此，客户可能有一种感知,他们可以用更少的时间和精力得到更好的网上交易。
这个创新的系统特性已被定义为知觉有用性。
若干实证研究发现，客户感知的实用性在采用影响满意度的创新技术后得以实现.因此，假设网上购物的知觉有用性与满意度成正相关(H1).网上客户首要关注的是涉及关于网上信用卡使用的明显的不安全感。
虽然认证系统有明显进步,但是顾客担心在网上传输信用卡号码这些敏感的信息是不会被轻易的解决的。
网上的隐私保护环境是另一个值得关注的问题。
研究表明,网上客户担心通过这些网上业务会造成身份盗窃或冒用他们的私人信息。
因此，据推测，网上购物的安全性对顾客满意度有积极地影响(Ｈ2)。
以往的研究表明,系统方面的技术,如网络速度,错误恢复能力和系统稳定性都是导致客户满意度的重要因素。
例如,Kｉm和Lim(20０1)发现,网络速度与网上购物者的满意度有关.Ｄelｌaert和卡恩（199９年)也报告说,当网络提供商没有进行很好的管理时网上冲浪速度慢会给评价网站内容带来负面影响。
丹尼尔和Alaｄwani的文件表明，系统错误的迅速准确的恢复能力以及网络速度是影响网上银行用户满意度的重要因素(H3)。
由于网上交易的非个人化性质客户查询产品和其他服务的迅速反应对客户满意度来说很重要。
人教版高一英语必修一Unit2知识梳理、重点词汇解析
人教版高一英语必修一Unit2知识梳理、重点词汇解析常言说:万事开头难。
还常说,能有一个好的开头,就是胜利的一半;即使不能有一半的胜利,也为今后打下好的基础。
进入高一学好英语打好基础很重要，以下是整理的高一英语学习文章。
人教版高一英语必修一Unit2学问梳理、重点词汇解析及单元自测Unit2一、学问点1. go to the pictures去看电影(美);go to the movies 去看电影(英)2. list the countries tht use English s n officil lnguge 列举把英语用作官方语言的GJ3. the rod to 通向之路4. t the end of在末端，在尽头，by the end最终(=finlly)5. becuse of 因为(留意和becuse 的区分)Mny beutiful fish re fst disppering becuse of the severe pollution.因为污染严峻，很多秀丽的鱼类正在面临绝种。
n rgument ws inevitble becuse they disliked ech other so much.争辩是不行幸免的，因为他们彼此特别厌恶。
6. ntive English spekers 以英语作为母语的人7. even if (= even thoug)即使，用来引导一个让步状语从句，后面既可用陈述语气，也可用虚拟语气，但是even if/even though,引导的从句中不用将来时。
如：Even though/if it rins tomorrow, we will leve for Beijing.8. come up 走上前来，走近，发生，出现come up with 追上，赶上，提出9. ctully ll lnguges chnge nd develop when cultures meet nd communicte with ech other.事实上，当不同文化互相沟通渗透时，全部的语言都会有所进展、有所改变。
The impacts of population change on carbon emissions in China during 1978–2008_1
The impacts of population change on carbon emissions in China during 1978–2008Qin Zhu ⁎,Xizhe Peng 1The State Innovative Institute for Public Management and Public Policy Studies,Fudan University,Shanghai 200433,Chinaa b s t r a c ta r t i c l e i n f o Article history:Received 29December 2011Received in revised form 15March 2012Accepted 27March 2012Available online 7May 2012Keywords:Carbon emission Population growth STIRPAT model Ridge regressionThis study examines the impacts of population size,population structure,and consumption level on carbon emissions in China from 1978to 2008.To this end,we expanded the stochastic impacts by regression on population,af fluence,and technology model and used the ridge regression method,which overcomes the negative in fluences of multicollinearity among independent variables under acceptable bias.Results reveal that changes in consumption level and population structure were the major impact factors,not changes in population size.Consumption level and carbon emissions were highly correlated.In terms of population structure,urbanization,population age,and household size had distinct effects on carbon emissions.Urbanization increased carbon emissions,while the effect of age acted primarily through the expansion of the labor force and consequent overall economic growth.Shrinking household size increased residential consumption,resulting in higher carbon emissions.Households,rather than individuals,are a more reasonable explanation for the demographic impact on carbon emissions.Potential social policies for low carbon development are also discussed.©2012Elsevier Inc.All rights reserved.1.IntroductionDuring the past 200years,global population,global income (gross domestic product),and carbon emissions have increased 6,70,and 20times,respectively (Jiang and Hardee,2009).The history of most developed countries shows that in the development process,industry accounts for the largest proportion of carbon emissions.However,recent statistics reveal that since the 1990s,the contribution of residential energy consumption in some developed countries to carbon emissions has exceeded that of industrial sectors.Therefore,the impacts of population growth and associated residential consump-tion on carbon emissions have attracted increasing research interest (Bin and Dowlatabadi,2005;Druckman and Jackson,2009;Weber and Adriaan,2000).Clearly identifying the relationship between population and carbon emissions is highly challenging primarily because of the wide-ranging effects of population on carbon emissions.These effects usually exert indirect in fluence over consumption,production,technology,and trade,among others.In terms of population characteristics,almost all important demographic factors,including population size,structure,quality,distribution,and migration,constantly change,thereby impos-ing complicated and variable effects on carbon emissions.Studies have thus far concentrated on the relationship between population growth and emission increase,as well as on the impacts of population structure,including age structure,urbanization level,regional distribution,and household composition,on carbon emissions.The approaches to studying the relationship between population and carbon emissions can be categorized into two:investigating the causalities and mechanisms of interaction between population and carbon emissions,and quantitatively evaluating the impacts of population growth on carbon emission increase.Birdsall (1992)summarized two principal mechanisms through which population growth in developing countries contributes to greenhouse gas emissions.The first is the effect of large populations on fossil fuel consumption —an effect that stems from the increased energy demand for power generation,industry,and transport.The second mechanism is the effect of population growth-related emissions on deforestation.The author concluded that reductions in population growth matter,but are not the key factor in leveling off carbon emissions.Knapp and Mookerjee (1996)discussed the nature of the relationship between global population growth and CO 2emissions by conducting a Granger causality test on annual data for 1880–1989.The results suggest no long-term equilibrium relationship,but imply a short-term dynamic relationship between CO 2emissions and population growth.The IPAT identity (Ehrlish and Holdren,1971)has been extensively used in the quantitative evaluation of the effects of population growth on carbon emission increase.According to the principle of the formula and its stochastic form,the stochastic impacts by regression on population,af fluence,and technology (STIRPAT)model,the main driving forces behind environmental impact (I)are population (P),af fluence (A),and technology (T).Researchers typically assess the impact of population on carbon emissions by altering population size while keeping other variables constant.Shi (2003)examined 1975–1996data on 93countries using the IPAT model and found that the impact of population change on carbon emissions is considerably more pronounced in developing countries than in developed nations.The author also determined thatEnvironmental Impact Assessment Review 36(2012)1–8⁎Corresponding author.Tel.:+862155665490;fax:+862155665211.E-mail addresses:zhuqin@ (Q.Zhu),xzpeng@ (X.Peng).1Tel.:+862155664676;fax:+862155665211.0195-9255/$–see front matter ©2012Elsevier Inc.All rights reserved.doi:10.1016/j.eiar.2012.03.003Contents lists available at SciVerse ScienceDirectEnvironmental Impact Assessment Reviewj o u r n a l h o me p a g e :w ww.e l s e v i e r.c om /l oc a t e /e i a rthe elasticity of emissions with respect to global population change was1.42.Cole and Neumayer(2004)and Rosa et al.(2004)also measured the impact of population on carbon emissions using the IPAT model,and found that the elasticities of emissions in relation to population were0.98and1.02,respectively.Wei(2011)discussed the role of technology in the STIRPAT model,and argued that the different functional forms of STIRPAT can explain the differences among estimates in studies on the environmental impacts of population and affluence.The effects of population on carbon emissions are commonly embodied in production and consumption behaviors,which are closely tied to population size and population structure.Satterthwaite (2009)investigated the CO2emission levels in various nations for the periods1950–1980and1980–2005.The results show little association between rapid population growth and high emission increase because nations with very low emissions per capita are mostly those with the highest population growth rates.Jiang and Hardee(2009)argued that consumption and production patterns among various population groups differ.In almost all climate models,however,population size is the only demographic variable considered.The assumption behind this treatment is that each individual in a population shares the same production and consumption behavior,but this assumption may be inaccurate and misleading.Hence,paying more attention to the variables of population structure is necessary in investigating the impact of population on carbon emissions.Researchers have closely monitored urbanization levels because these are highly relevant to residential consumption scale and consumption structure.Urbanization generally affects carbon emissions in three ways. First,the use of energy in production is concentrated primarily in cities, and residential consumption level increases in line with urbanization. Both situations increase energy demand,resulting in carbon emission increase,given that the energy structure remains the same.Second,the requirements for infrastructure and dwelling houses grow along with urbanization,increasing the demand for building materials(especially cement products),which are important sources of carbon emissions. Third,urbanization involves the conversion of grasslands and woodlands, these land-use changes increase carbon emissions.Poumanyvong and Kaneko(2010)empirically investigated the effects of urbanization on energy use and CO2emissions.In the investigation,the authors considered different development stages using the STIRPAT model, as well as a balanced panel dataset that covers1975–2005and includes 99countries.Thefindings suggest that the impact of urbanization on carbon emissions is positive for all income groups,but that this effect is more pronounced in the middle-income group than in the other income groups.Pachauri and Jiang(2008)compared the household energy transitions in China and India since the1980s by analyzing aggregate statistics and nationally representative household surveys.The authors revealed that compared with rural households,the urban households in both nations consumed a disproportionately large share of commercial energy and were much further along in the transition to modern energy.Satterthwaite(2009)considered the implications of population growth and urbanization for climate change between1980and2005. The author concluded that the increasing number of urban consumers and their consumption levels,not population growth,drive the increase in greenhouse gas emissions.Studies on the relationship between age structure and carbon emissions focus on the accelerated global aging process.Research in this area is still at its infancy.Fan et al.(2006)analyzed the impact of population,affluence,and technology on the total CO2emissions of countries at different income levels at the global scale over the period 1975–2000.The results show that population age(15–64years)has less impact on CO2emissions than do population size,affluence,and technology.Dalton et al.(2008)incorporated population age structure into an energy–economic growth model with multiple dynasties of heterogeneous households to estimate and compare the effects of aging populations and technical change on the baseline paths of US energy use and CO2emissions.The authors showed that an aging population reduces long-term emissions by almost40%in a low-population scenario,and that the effects of the aging process on emissions can be as large as,or larger than,those of technical change in some cases, given a closed economy,fixed substitution elasticity,andfixed labor supply over time.The effect of changes in household size on carbon emissions is another research focus.Given afixed population size,a change in the number of households due to a change in household size can influence consumption scale and consumption structure,thereby significantly affecting carbon emissions.Thus far,there is no commonly accepted standard for defining household types in terms of environmental influence,and the effect of changes in household size on carbon emissions remains uncertain.Dalton et al.(2007)incorporated household size into the population–environment–technology model to simulate economic growth,as well as changes in the consumption of various goods,direct and indirect energy demand,and carbon emissions over the next 100years.Jiang and Hardee(2009)discussed the impact of shrinking household size on carbon emissions and argued that households,rather than individuals in a population,should be used as the variable in analyzing demographic impact on emissions.This approach is favorable considering that households are the units of consumption,and possibly also the units of production in developing societies.China is currently at a demographic turning point,i.e.,changing from an agricultural into an urban society,from a young society to an old one,and from a society attached to land to a morefloating one (Peng,2011).Population dynamics and changes in consumption patterns have influenced and will undoubtedly continue to influence China's energy use and consequent carbon emissions.Examining these issues will facilitate improvements in decision making for low carbon development.In this study,therefore,we incorporate population structure(age structure,urbanization level,and household size)into the STIRPAT model to examine the impacts of population size,population structure,and consumption level on carbon emissions.By doing so, we hope to more completely and accurately reflect the impacts of population change on carbon emissions.To overcome the negative influences of multicollinearity among independent variables,we use the ridge regression method to estimate the coefficients of the model. As an empirical case study,the impacts of population and consumption on emissions in China from1978to2008are quantitatively assessed and analyzed.Corresponding policy suggestions for energy conserva-tion and emission reduction in China are proposed.2.ModelThe IPAT identity(Ehrlish and Holdren,1971)is an equation that is commonly used to analyze the impacts of human behavior on environmental pressure.The equation is expressed asI¼PAT;ð1Þwhere I represents environmental impact,P represents population,A stands for affluence,and T denotes technology.The IPAT identity is an accounting model,in which one term is derived from the values of the three other terms.The model requires data on only any three of the four variables for one or a few observational units,and it can only be used to measure the constant proportional impacts of the independent variables on the dependent variable.To overcome this limitation,Dietz and Rosa(1994)established the STIRPAT model by reformulating the IPAT identity into stochastic form:I¼aP b A c T d e;ð2Þwhere I,P,A,and T have the same definitions as in the IPAT identity;a,b, c,and d are coefficients;and e is a residual term.In this reformulation, data on I,P,A,and T can be used to estimate a,b,c,d,and e with statistical2Q.Zhu,X.Peng/Environmental Impact Assessment Review36(2012)1–8regression methods.The reformulated version can convert the IPAT accounting model into a general linear model,in which statistical methods can be applied to test hypotheses and assess the non-proportionate importance of each in fluencing factor.As a special case,the stochastic version can be converted back to the original model given that a =b =c =d =e =1.York et al.(2003)developed an additive regression model in which all variables are in logarithmic form,facilitating estimation and hypothesis testing.York et al.(2003)and Wei (2011)argued that in the typical application of the STIRPAT model,T should be included in the error term,rather than separately estimated,for consistency with the IPAT model,where T is solved to balance I ,P ,and A .The modi fied STIRPAT model is expressed as follows:ln I ¼ln a þb ln P ðÞþc ln A ðÞþe :ð3ÞAccording to the concept of ecological elasticity (York et al.,2003),coef ficients b and c from Eq.(3)are the population and af fluence elasticities,respectively.These elasticities refer to the responsiveness or sensitivity of environmental impacts to changes in corresponding impact factors.For instance,coef ficient b indicates percentage change in I in response to a 1%change in population,with other factors held constant.To comprehensively observe the impact of population on carbon emissions,we incorporate the indicators of population structure,including urbanization level,age structure,and household size,into the STIRPAT model to come up with the following expanded form:ln I ¼ln a þb s ln Ps ðÞþb c ln Pu ðÞþb a ln Pw ðÞþb f ln Ph ðÞþc ln A ðÞþe ;ð4Þwhere–I refers to carbon emissions;–Ps denotes population size;–Pu ,Pw ,and Ph are the three factors that indicate population structure;that is,Pu for urbanization rate,Pw for the proportion of working age (16–64years old)population,and Ph for household size,which is indicated by the average number of household members;–A represents per capita annual expenditure;–e is a residual term.3.Data description and data testing 3.1.Data descriptionThe population,consumption,and carbon emissions in China from 1978to 2008are summarized in Table 1.Data on carbon emissions from fossil fuels and cement come from the data center of the Carbon Dioxide Information Analysis Center of Oak Ridge National Laboratory,USA (CDIAC,2011).Population and consumption data are obtained from the China Statistical Yearbook,released by China's National Bureau of Statistics.Expenditure data are adjusted to fit the fixed prices in 2000.Fig.1shows the changing rates of all the variables,with 1978as the base year.Almost all the variables were non-stationary,with a continuous uptrend or downtrend during the period.Among all the variables,per capita expenditure presented the fastest growth at 8.17times,followed by carbon emissions (3.72times)and urbanization rate (1.55times).Population size and proportion of working age population increased by 37.96%and 22.35%,respectively.Average household size showed a continuous shrinking trend,decreasing by 32.24%over the period.Taking the logarithm of data can reduce non-stationarity,as well as linearize variables,so that the disadvantage presented by variables having different measurement units is eliminated;thus,all the data used in the current work are transformed into natural logarithmic series.3.2.Stationarity testThe acceptability of a regression result is commonly based on the premise that the series used in the regression model are stationary or co-integrated if the series are non-stationary;otherwise inauthentic regression may occur.Furthermore,multicollinearity among indepen-dent variables can cause large variances in estimated coef ficients and decrease the accuracy of estimated equations;a multicollinearity test should be performed on independent variables.The augmented Dickey –Fuller (ADF)unit root test is typically used to examine the stationarity of time series,in which a high-order autoregressive model with an intercept term is established (Maddala and Kim,1998).Taking the ADF test on series ln I as an example,we express the test equation with the constant term,as well as the trend and intercept terms,as follows:Δln I t ¼αþβt þδln I t −1þX k i ¼1βi Δln I t −i þεt ;ð5Þwhere α,β,and δare coef ficients;εis a residual term;and k is the lag length,which turns the residual term into a stochastic variable.The null hypothesis H 0is δ=0;i.e.,at least one unit root exists,causing the non-stationarity of the series.The test is conducted with three formulations:(α≠0,β≠0),(α=0,β≠0),and (α=0,β=0).As long as one of the three models rejects the null hypothesis,the series are considered stationary.However,when the results of all the three models do not reject the null hypothesis,the series are regarded as non-stationary.The results of the stationary test on all the series are summarized in Table 2.According to the results,series ln Pu ,ln Pw ,ln Ph ,and ln A are I (0)or stationary.Series ln Ps and ln I are I (1),indicating that they are first-order integrated series.Hence,the co-integration between the two series must be examined to determine whether they satisfy the precondition of regression analysis.3.3.Co-integration testSeries ln Ps and ln I are both I (1);thus,they satisfy the precondition of the same integrated order for conducting a bivariate co-integration test.On the basis of the Engle –Granger test method (Engle and Granger,1987),we express the co-integration regression equation as ln I t ¼αþβln Ps t þεt :ð6ÞDenoting the estimated regression coef ficients of Eq.(8)as ^αand ^β,the estimated residual series is then expressed as follows:^ε¼ln I t −^α−^βln Ps t:ð7ÞIf ^εis I (0),then ln I and ln Ps are co-integrated.Coef ficients ^αand ^βare estimated by ordinary least squares (OLS),and then the unit root test is performed on estimated residual series ^εusing the ADF test method.The results are shown in Table 3.Table 3shows that the calculated ADF t -statistic of series ^εwas −1.8455,which is less than the critical value at the 10%signi ficance level.Hence,the result rejects the null hypothesis,indicating thatseries ^εwithout a unit root is stationary;i.e.,^εis I (0).Therefore,series ln I and ln Ps are co-integrated.We examine the Granger causality between series ln I and ln Ps .The bivariant regression models for the Granger causality test are expressed as follows:ln I t ¼α0þX k i ¼1αi ln I t −i þX k i ¼1βi ln Ps t −i ;ð8Þ3Q.Zhu,X.Peng /Environmental Impact Assessment Review 36(2012)1–8ln Ps t ¼α0þX k i ¼1αi ln Ps t −i þX k i ¼1βi ln I t −i :ð9ÞThe null hypothesis is β1=β2=…=βk =0given that the maximal lag length is k =2.The test results are shown in Table 4.The first hypothesis states that series ln Ps is not the Granger cause of series ln I ;the concomitant signi ficance of this hypothesis was 0.1619,suggesting that ln Ps is the Granger cause of ln I ,with 83.81%signi ficance.The concomitant signi ficance for the second hypothesis was 0.8752,indicating that ln I is not the Granger cause of ln Ps .3.4.Multicollinearity testMulticollinearity refers to a situation in which two or moreindependent variables in a multiple regression model are highly linearly related (Donald and Robert,1967).In this situation,the standard errors of the affected coef ficients tend to be large,and the coef ficient estimates may change erratically in response to small changes in data.Such erratic changes result in the possible failure of the regression model to provide valid results on individual variables.The multicollinearity of the independent variables in the model is examined by OLS regression and by valuing the variance in flation factors (VIFs)of the variables.Taking the test on multicollinearity among ln Ps and the other variables as an example,we use the OLS method to regress ln Ps on the other independent variables.As shown in Table 5,the estimated coef ficient of determination (R 2)of the model was 0.9803and the F -test was highly signi ficant,with an F -statistic of 323.8751at the 0.1%signi ficance level.The VIFs of the variables ranged from 29.6551to 173.5764,which are considerably greater than 10.Given that Marquardt (1970)used a VIF greater than 10as a guideline for severe multicollinearity,we can conclude that a high degree of-100%0%100%200%300%400%500%600%700%800%900%1978198019821984198619881990199219941996199820002002200420062008YearC h a n g i n g R a t eFig.1.Changing rates of population,consumption,and carbon emissions in China (1978–2008).Sources:same as in Table 1.Table 1Population,consumption,and carbon emissions in China (1978–2008).Year Carbon emissions (MtC)a Population size (104)Urbanization rate (%)Proportion of working age population (%)Household size(person/household)Per capitaexpenditure (CNY)197840,768.996,25917.9259.50 4.66740197941,648.997,54218.96%60.00% 4.65791198040,698.698,70519.39%60.50% 4.61862198140,292.5100,07220.16%61.00% 4.54934198243,122.8101,65421.13%61.50% 4.51997198345,468.6103,00821.62%62.37% 4.461079198449,433.6104,35723.01%63.24% 4.411207198553,587.3105,85123.71%64.12% 4.331370198656,348.0107,50724.52%64.99% 4.241435198760,123.0109,30025.32%65.86% 4.151520198864,445.3111,02625.81%66.15% 4.0516********,473.6112,70426.21%66.45% 3.971635199065,855.4114,33326.4166.74 3.931695199169,147.7115,82326.9466.30 3.891842199272,143.5117,17127.4666.20 3.852*********,019.8118,51727.9966.70 3.812262199481,807.1119,85028.5166.60 3.782367199588,471.7121,12129.0467.20 3.742553199692,597.1122,38930.4867.20 3.722793199791,486.8123,62631.9167.50 3.642919199886,614.1124,76133.3567.60 3.633091199990,501.7125,78634.7867.70 3.583346200092,886.8126,74336.2270.15 3.443632200195,144.0127,62737.6670.40 3.4238552002100,957.7128,45339.0970.30 3.3941252003118,724.4129,22740.5370.40 3.3844152004139,067.5129,98841.7670.92 3.3147732005153,424.4130,75642.9972.04 3.2451422006166,458.9131,44843.9072.32 3.1756362007180,165.9132,12944.9472.53 3.1762392008192,268.7132,80245.6872.803.166782Sources:The carbon emission data are obtained from the CDIAC (2011);the data on population and consumption are from the China Statistical Yearbook,with some interpolation for the missing data on working age population for several years in the 1980s;the expenditure data are adjusted to fit the fixed prices in 2000.aMtC refers to million-ton carbonTable 2Results of the stationary test using the ADF test.Variable Difference order Exogenous (α,β,k )t -Statistic Signi ficance level Test critical value Verdictln Ps1(α,β,1)−3.71955%−3.5806I (1)ln Pu 0(0,0,1)−2.94401%−2.6471I (0)ln Pw 0(0,0,1)−3.25631%−2.6471I (0)ln Ph 0(0,0,1)−4.05991% 2.6471I (0)ln A 0(α,β,1)−3.298310%−3.2217I (0)ln I1(α,0,4)−3.29725%−2.9862I (1)4Q.Zhu,X.Peng /Environmental Impact Assessment Review 36(2012)1–8multicollinearity exists among ln Ps and the other independent variables in Eq.(4).The same multicollinearity test was performed on the other independent variables;all the results indicate a high degree of multicollinearity among these variables.4.Regression estimation4.1.Ridge regressionThe danger of multicollinearity primarily stems from its generation oflarge standard errors among related independent variables;these errors are characterized by large variances in model parameters,making the model unstable.Given that these standard errors are significantly reduced using a curtain method,the negative consequences of such errors can be effectively eliminated even when multicollinearity remains in the model.Ridge regression,which can obtain acceptably biased estimates with smaller mean square errors in independent variables through tradeoffs in bias–variance,is one of the most effective solutions for multicollinearity.Hoerl and Kennard(1970)explicitly specified the estimation procedure for ridge regression as an improved substitute for traditional OLS regression.Consider the standard model for multiple linear regression,Y¼Xβþε;ð10Þwhere X is(n×p)and is of rank p,βis(p×1)and unknown,E[ε]=0,and E[εε′]=δ2I.The unbiased estimate ofβis normally given by^β¼X′XðÞ−1X′Y:ð11ÞWhen a high degree of multicollinearity exists among X,the X′X matrix is ill-conditioned;i.e.,the value of its determinant|X′X|≈0, and attempts to calculate the(X′X)−1matrix may be highly sensitive to slight variations in data.In controlling the inflation and general instability associated with least squares estimates,as well as in estimatingβ,the ridge regression that incorporates small positive quantity k to the diagonal of normalized independent variable matrix X′X uses^βüX′XþkIðÞ−1X′Y:ð12ÞThis equation creates a variance in parameter estimates that is less than that estimated by OLS regression under the condition k≥0.Therefore,choosing an appropriate k,accepting minimal bias,and substantially reducing variance are possible,thereby remarkably improving estimation.Ridge regression can be converted back to OLS regression as a special case given that k=0(Hoerl and Kennard,1970).Considering that the relationship of a ridge estimate to an ordinary estimate is given as^βüIþk X′XðÞ−1h i−1^β;ð13Þwe can derive the expression for estimating the bias introduced when ^βÃis used rather than^βas follows:bias¼Iþk X′XðÞ−1h i−1:ð14Þ4.2.Estimation resultsThe ridge traces estimated for the expanded STIRPAT model are shown in Fig.2.The results for all the estimated normalized coefficients are summarized in Table6.As shown in Fig.2,when k=0.20,the coefficients of the indepen-dent variables tend to be stable.In this situation,the model exhibited a high goodness-of-fit,with an adjusted coefficient of determination (R2)of0.9454.The F-test of the model was highly significant,with an F-statistic of104.8277at the0.1%significance level.All the estimated coefficients passed the significance tests with t-statistic at the0.1% significance level.The VIFs of the estimated coefficients ranged from 0.1704to0.4791,all much lower than10.The bias introduced wasTable4Results of the Granger causality test on ln I and ln Ps.Null hypothesis:Obs F-statistic Probabilityln Ps is not the Granger cause of ln I29 1.96630.1619ln I is not the Granger cause of ln Ps0.13400.8752Table5Multicollinearity test on ln Ps and other independent variables by OLS.Adjusted R20.9803 Standard error0.0155F-statistic323.8751⁎⁎⁎ln Ps Coefficient t-Statistic VIFln Pu−0.2706⁎−2.4252120.7909(0.1116)ln Pw0.3378 1.274929.6551(0.2650)ln Ph−0.5381⁎−2.3791100.8291(0.2262)ln A0.1383⁎ 2.4129173.5764(0.0573)Constant11.1249⁎⁎⁎15.3781(0.7234)Standard errors are in parentheses.⁎⁎⁎p b0.001(two-tailed test).⁎p b0.05(two-tailed test).-1120.000.050.100.150.200.250.300.350.400.450.50kNormalizedcoefficientFig.2.Ridge trace estimated for Eq.(4).Table3Results of the unit root test on^ε.Significance t-Statistic ProbabilityADF test statistic−1.84550.0626 Test critical values:1%level−2.64715%level−1.952910%level−1.61005Q.Zhu,X.Peng/Environmental Impact Assessment Review36(2012)1–8。
结果英语作文模板万能
结果英语作文模板万能英文回答：Introduction。
The results section of a research paper is the culmination of all the hard work that has been put into the study. It is where the researcher presents the findings of the study in an organized and concise manner. The results section should be written in a way that is easy for the reader to understand and interpret.There are a few key elements that should be included in a results section. These elements include:A brief overview of the study。
A description of the methods used to collect and analyze the data。
A presentation of the results of the study。
A discussion of the implications of the findings。
Overview of the Study。
The overview of the study should provide the reader with a brief understanding of the purpose of the study, the research questions that were addressed, and the methodsthat were used to collect and analyze the data. This overview should be written in a clear and concise manner, and it should be no more than a few paragraphs in length.Description of the Methods。
whether的用法总结_whether的用法例句
whether的用法总结大家在学习英语的时候，应该会常常使用到whether这个单词，所以了解whether的用法十分有必要，那么whether的用法有哪些?下面是小编给大家带来的whether的用法总结_whether的用法例句，以供大家参考，我们一起来看看吧!whether的意思pron. 其中的哪一个n. 可能的选择▼whether的用法总结whether可以用作连词whether用作连词,意思是“是否,是不是”,可引导名词从句或动词不定式短语。
whether还可引导让步状语从句,意思是“不管,无论”,从句中通常用一般现代时代替将来时。
whether用作连词的用法例句I'm uncertain whether to go or not.我不能肯定去还是不去。
His nationality isn't relevant to whether he's a good teacher.他的国籍与他是否是位好老师无关。
Little does he care whether we live or die.他一点也不管我们是死是活。
▼whether的用法例句1、The key issue was whether the four defendants acted dishonestly.关键问题是4名被告是否存在欺诈行为。
2、It's not a case of whether anyone would notice or not.这不是会不会有人注意到的问题。
3、Whether such properties are a good deal will depend on individual situations.这样的地产是否可获得大笔收益还要视具体情形而定。
▼whether的短语whether or no是否whether or是否whether or not是否no matter whether无论是否not know whether to laugh or cry(面对恶劣或不幸情况)不知所措, 哭笑不得not know whether you're coming or going(激动得)不知如何是好, 不知所措▼whether和if辨析whether 和 if 都可以作“是否”解，但二者在具体使用时有一定的区别。
Unit2 Improving Yourself 第 2 课时(Using Language
(3) 用在主句动词是过去式时的宾语从句中。 e.g. He wanted to know if the result had been announced (宣布). He told us that the project had been completed.
(4) 根据语境，判断动作的先后顺序或被动情况。 e.g. It was the first time he had been bitten by a dog.
e.g. The vegetables didn’t taste good. They had been cooked for too long.
(2) 表示从过去某一时间开始，延续到过去另一时间的被动 动作，常与for, since引导的时间状语连用。 e.g. It was reported that the money had been raised for weeks.
Jack started the habit of cleaning his room last week. This is what his room looked like yesterday morning. (The floor was clean. The sheet was laid flat on the bed. all the books and clothes were placed neatly and tidily. The pictures on the wall were straight.) But two weeks ago, Jack’s room had been an awful mess. His clothes had been thrown everywhere, the pictures had not been hung straight, and all the drawers had been opened. It had been a very disorderly sort of room, with books and papers lying around everywhere.
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Whether do the principal results of the traditional theory of trade still hold in the presence of fragmentation, outsourcing and off-shoring intermediate inputs and services?
trade in tasks and fragmentation
Definition : revolutionary advances in transportation and communication’s technology have enabled an historic break-up of the production process by making it increasingly viable and profitable for firms to undertake different production stages in disparate locations .this has resulted in off-shoring of both services and manufacturing sector jobs and rapidly growing trade intermediate products or tasks. This phenomenon has various been called trade in tasks and fragmentation.
Two approaches to the modeling of fragmentation: ① model fragmentation as trade in intermediate based on comparative advantage. the main insight is that offshoring is similar to technical progress in the production of final goods. Model: only two nation : A and B one final good : X one single production factor: labor two task: 1 & 2 A→1 b→2 so with free trade in tasks ,A specializes in the production of task 1,and B specializes in the production of task 2. more final good can be produced in both countries. that is to say ,off-shoring increases labour productivity.
#Intermediate inputs #services # trade in tasks and fragmentation
Intermediate inputs
Deardorff: only an average relationship between comparative advantage and trade seems to be at all robust but the gains from trade are unambiguous in the Ricardian models. that’s so in the H-O case. Kemp: Stolper-Samuelson and the Rybczynski theorems still hold in the presence of the trade intermediate products. Schweinberger: under the condition the H-O theorem holds, when each final good can be used as intermediate input in the production of other final good.
North firms are interested in combining their superior technology with cheap labour in south they will offshore a task if the initial wage is larger than the offshoring costs. Therefore, the wage in North will increase because productivity increases .productivity increases because offshoring releases domestic workers who can focus on the tasks where they have a trade-costadjusted comparative advantage. The productivity effect is independent of comparative advantage based on tasks and comes together with the Ricardian gains from trade.
Deadorff : three characteristics of trade in services. ① arise as by-product of trade in goods. ② accompanied by international direct invest. ③ where they are produced is the same one where they are consumed . The first two can show the usefulness of the law of comparative advantage in explaining trade . Even though the third causes some difficulties ,the weak versions of the law may apply by relying on specific assumptions. Role of service: The benefit from service liberalization extend beyond the traditional gains from trade liberalization. And service liberalization can stimulate international trade of goods. The trade in services is important to facilitate trade in goods . There exists complementarity between trade in goods and trade .
services
Hindley and Smith: through the application of the normative theory of comparative cost to the service sector ,even though there are difficulties ,the powerful logic of the theory of comparative advantage comes through them and shows gains from free trade in service . Melvin : adding capital services as tradable in a H-O framework . though the view seem to contradict the law of comparative advantage and H-O theorem at first glance ,in fact it conform with them.