本文選題：碳排放影響因素　切入點(diǎn)：最小二乘支持向量機(jī)　出處：《華北電力大學(xué)》2017年碩士論文

【摘要】：能源消費(fèi)一直是人類經(jīng)濟(jì)發(fā)展和社會(huì)進(jìn)步的重要驅(qū)動(dòng)力,特別是對(duì)中國(guó)來(lái)說(shuō),煤炭和原油等化石燃料主導(dǎo)了國(guó)家人民的生產(chǎn)生活。但是,在全球氣候變化日益嚴(yán)峻以及低碳可持續(xù)發(fā)展經(jīng)濟(jì)興起的背景下,能源消費(fèi)過(guò)程中隨之帶來(lái)的二氧化碳排放問(wèn)題已經(jīng)不可忽視。作為第一的發(fā)展中國(guó)家,中國(guó)如何在保證國(guó)內(nèi)經(jīng)濟(jì)又好又快發(fā)展的同時(shí),確保能源安全供給、減少碳排放量、應(yīng)對(duì)氣候變化以及承擔(dān)國(guó)際環(huán)境保護(hù)義務(wù)與責(zé)任,成為亟待解決的一大問(wèn)題。河北省作為中國(guó)大省,近些年來(lái),其環(huán)境問(wèn)題極其嚴(yán)重,特別是霧霾污染問(wèn)題�？紤]到碳排放現(xiàn)象是引起霧霾的一大重要因素,對(duì)河北省影響碳排放因素的研究是十分必要的。在本文中,首先介紹了與碳排放相關(guān)的概念與理論,中國(guó)碳排放現(xiàn)狀以及目前國(guó)內(nèi)外對(duì)于碳排放研究的具體情況。其次,本文介紹了一種適合小樣本數(shù)據(jù)的最小二乘支持向量機(jī)算法,針對(duì)算法懲罰因子和核函數(shù)寬度根據(jù)經(jīng)驗(yàn)確定的問(wèn)題,本文引入改進(jìn)的螢火蟲(chóng)算法,利用啟發(fā)式智能算法進(jìn)行尋優(yōu),在此基礎(chǔ)上提高算法的精確度。在實(shí)證研究部分,本文選擇了1990-2014年中國(guó)河北省的碳排放量和影響因子相關(guān)數(shù)據(jù)為研究對(duì)象。對(duì)于預(yù)選的多個(gè)影響因子,本文采用SPSS統(tǒng)計(jì)軟件確定因子的顯著相關(guān)性。然后根據(jù)樣本數(shù)據(jù)的特點(diǎn),運(yùn)用本論文提出的算法針對(duì)性進(jìn)行建模。為了更好地定量衡量因子對(duì)碳排放量的具體影響,后續(xù)還采用了可拓展的隨機(jī)性的環(huán)境影響評(píng)估模型(STIRPAT)與對(duì)數(shù)平均迪氏分解模型(LMDI)深入分析碳排放量與影響因子之間的關(guān)聯(lián)性。根據(jù)分析結(jié)果,可以得知:(1)與其他三種算法對(duì)比,本文提出的新算法驗(yàn)證了河北省碳排放量與經(jīng)過(guò)SPSS篩選確定的十三個(gè)影響因子之間的因果關(guān)系,證明了本文提出的算法對(duì)標(biāo)準(zhǔn)螢火蟲(chóng)算法的改進(jìn)和對(duì)最小二乘支持向量機(jī)的優(yōu)化是有效的。(2)STIRPAT模型結(jié)果表明河北省碳排放量與本文選定的13個(gè)影響因子之間均為正向相關(guān),其中最終消費(fèi)的驅(qū)動(dòng)指數(shù)最大,交通運(yùn)輸工具擁有量的指數(shù)最小。(3)利用LMDI分解法對(duì)碳排放系數(shù)、能源強(qiáng)度、能源消費(fèi)結(jié)構(gòu)、產(chǎn)業(yè)結(jié)構(gòu)、經(jīng)濟(jì)活動(dòng)規(guī)模和人口規(guī)模這六個(gè)影響因素進(jìn)行分解,得知經(jīng)濟(jì)活動(dòng)規(guī)模效應(yīng)是所有因素中對(duì)河北省碳排放總量影響最大的正向推動(dòng)因素。最后,本文還從能源結(jié)構(gòu)、能源效率、人口政策、交通工具和低碳理念層面提出了一些針對(duì)性的建議,有利于在理論層面上為政府制定減排政策提供支持,并從源頭上有效控制碳排放的產(chǎn)生。
[Abstract]:Energy consumption has been an important driving force for human economic development and social progress, especially for China, where fossil fuels such as coal and crude oil dominate the production and livelihood of the country's people. In the context of the increasingly severe global climate change and the rise of low-carbon sustainable development economy, the carbon dioxide emissions brought about by the energy consumption process can not be ignored. As the first developing country, How can China ensure a sound and rapid development of its domestic economy while ensuring a secure supply of energy, reducing carbon emissions, combating climate change and assuming international environmental protection obligations and responsibilities? Hebei Province, as a big province in China, has been facing serious environmental problems in recent years, especially haze pollution. Considering that carbon emission is one of the most important factors causing haze, It is necessary to study the factors affecting carbon emissions in Hebei Province. In this paper, the concepts and theories related to carbon emissions, the current situation of carbon emissions in China and the specific situation of carbon emission research at home and abroad are introduced. This paper introduces a least squares support vector machine (LS-SVM) algorithm for small sample data. Aiming at the problem that penalty factor and kernel width are determined by experience, an improved firefly algorithm is introduced in this paper. Heuristic intelligent algorithm is used to optimize and improve the accuracy of the algorithm. This paper selects the data of carbon emissions and impact factors from 1990 to 2014 in Hebei Province, China as the research object. For the pre-selected factors, we use the SPSS software to determine the significant correlation of the factors. Then according to the characteristics of the sample data, In order to better measure the specific impact of factors on carbon emissions, In the follow-up, the extended stochastic environmental impact assessment model (STIRPAT) and the logarithmic average Dickers decomposition model (LMDI) were used to analyze the correlation between carbon emissions and impact factors in depth. According to the results of the analysis, we can see that: 1) is compared with the other three algorithms. The new algorithm proposed in this paper verifies the causal relationship between the carbon emissions in Hebei Province and the thirteen influence factors determined by SPSS screening. It is proved that the improvement of the standard firefly algorithm and the optimization of the least square support vector machine (LS-SVM) are effective. The results show that the carbon emissions of Hebei Province are positively correlated with the 13 factors selected in this paper. Among them, the driving index of final consumption is the largest, the index of ownership of transportation vehicles is the smallest. (3) the carbon emission coefficient, energy intensity, energy consumption structure and industrial structure are analyzed by LMDI decomposition method. By decomposing these six factors, the scale of economic activity and the size of population, we know that the scale effect of economic activity is the most positive driving factor to the total amount of carbon emissions in Hebei Province. Finally, this paper also analyzes the energy structure and energy efficiency. Population policy, transportation and low-carbon concept level put forward some targeted suggestions, which is conducive to the theoretical support for the government to formulate emission reduction policies, and to effectively control the generation of carbon emissions from the source.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：X24;F426.2

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 唐德才;吳梅;;2013~2020年江蘇省碳排放驅(qū)動(dòng)因素趨勢(shì)預(yù)測(cè)[J];生態(tài)經(jīng)濟(jì);2016年01期

2 楊樹(shù)旺;劉航;覃志立;;霧霾防治背景下的中國(guó)碳排放權(quán)交易市場(chǎng)建設(shè)研究[J];理論與改革;2015年04期

3 王憲恩;史記;王泳璇;胡若漪;段海燕;;吉林省能源消費(fèi)碳排放變化趨勢(shì)預(yù)測(cè)與影響因素分析[J];環(huán)境科學(xué)與技術(shù);2015年06期

4 佟昕;陳凱;李剛;;中國(guó)碳排放影響因素分析和趨勢(shì)預(yù)測(cè)——基于STIRPAT和GM(1,1)模型的實(shí)證研究[J];東北大學(xué)學(xué)報(bào)(自然科學(xué)版);2015年02期

5 吳振信;石佳;;基于STIRPAT和GM(1,1)模型的北京能源碳排放影響因素分析及趨勢(shì)預(yù)測(cè)[J];中國(guó)管理科學(xué);2012年S2期

6 何建坤,劉濱;作為溫室氣體排放衡量指標(biāo)的碳排放強(qiáng)度分析[J];清華大學(xué)學(xué)報(bào)(自然科學(xué)版);2004年06期

7 能源研究所“中國(guó)可持續(xù)發(fā)展能源暨碳排放分析”課題組;中國(guó)可持續(xù)發(fā)展能源暨碳排放情景研究[J];中國(guó)能源;2003年06期

8 戴彥德,朱躍中;中國(guó)可持續(xù)能源發(fā)展情景及其碳排放分析[J];中國(guó)能源;2002年11期

9 Daniel A.Lashof,Dilip R.Ahufa,朱希敏;溫室氣體排放對(duì)全球變暖的相對(duì)貢獻(xiàn)[J];地球科學(xué)進(jìn)展;1991年03期

10 朱式玉;;控制“溫室效應(yīng)”[J];化學(xué)教育;1990年06期

相關(guān)博士學(xué)位論文 前1條

1 王文超;中國(guó)省區(qū)能源消費(fèi)與二氧化碳排放驅(qū)動(dòng)因素分析及預(yù)測(cè)研究[D];大連理工大學(xué);2013年

相關(guān)碩士學(xué)位論文 前5條

1 趙亞南;京津冀碳生產(chǎn)率分析研究[D];河北經(jīng)貿(mào)大學(xué);2015年

2 張希剛;山東省碳排放驅(qū)動(dòng)因素分析及趨勢(shì)預(yù)測(cè)[D];華北電力大學(xué);2015年

3 張夢(mèng)斯;京津冀地區(qū)碳排放因素分解及低碳經(jīng)濟(jì)發(fā)展路徑研究[D];北京理工大學(xué);2015年

4 余晶晶;中國(guó)城鎮(zhèn)化對(duì)碳排放的影響研究[D];北京交通大學(xué);2014年

5 董靜;螢火蟲(chóng)算法研究及其在水下潛器路徑規(guī)劃中的應(yīng)用[D];哈爾濱工程大學(xué);2013年

，

本文編號(hào)：1663442