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題名:台灣地區溫室氣體排放基線預測
作者:彭開瓊
作者(外文):Kai-Chiung Peng
校院名稱:國立臺灣大學
系所名稱:農業經濟學研究所
指導教授:徐世勳
林師模
學位類別:博士
出版日期:2002
主題關鍵詞:溫室氣體二氧化碳甲烷氧化亞氮基線狀態空間卡曼濾波TAIGEM-DGreenhouse gasCO2CH4N2Obaselinestate spaceKalman filterTAIGEM-D
原始連結:連回原系統網址new window
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鑒於京都議定書簽訂後,估計與預測精確的溫室氣體排放基線除了有助於暸解環境變遷的情形,更能作為國際間溫室氣體減量合作的基礎,本研究在探討國際上基線的定義與相關爭議後,選定能源部門、農業部門、廢棄物部門與其他部門等,先進行二氧化碳、甲烷與氧化亞氮三種主要溫室氣體1986年至2000年的歷史排放推估,然後再應用狀態空間模型與TAIGEM-D模型,進行2001年至2020年的未來排放預測,並對兩個模型的方法論與預測結果,進行比較分析與探討。
在台灣地區溫室氣體排放基線方面,我們針對能源部門、農業部門、一般廢棄物部門、事業廢棄物部門、能源部門漏損與交通部門分別進行分析,發現台灣地區過去15年(1986年至2000年)排放溫室氣體數量最多的部門為能源部門,約占總排放量的五成五至七成五左右,且比例年年上升;其次以事業廢棄物部門與一般廢棄物部門所占比例較高,約合占兩成五至四成之間,但其比例年年降低;而交通部門、農業部門與能源部門漏損所占比例較低,三者合占約5%左右。在預測結果方面,我們發現台灣地區未來20年(2001年至2020年)溫室氣體排放基線的結構與歷史結構類似,依比例來看順序仍為能源部門、事業廢棄物部門、一般廢棄物部門、交通部門、農業部門與能源部門漏損等;但仔細觀察其走勢可發現能源部門排放所占的比例不但最高,且其比例仍將不斷上升,因此政府若能訂定有效的能源需求管理政策,抑制含碳能源的消費,將可有效減緩台灣地區溫室氣體排放上升的趨勢。
在溫室氣體排放基線的氣體別結構方面,過去15年來以二氧化碳所占的比例最高,約占總排放量的五成五至七成五左右,且比例年年上升,其次則以甲烷排放所占比例較高,約合占兩成五至四成之間,但比例年年降低;至於氧化亞氮所占比例最低,一直保持在5%以下。在預測方面,我們發現台灣地區未來20年溫室氣體排放基線與過去15年類似,仍以二氧化碳所占的比例較高,且二氧化碳所占的比例會由2001年的八成多一直上升至九成五以上,而其主要原因在於能源消費的穩定成長,次要原因則為一般廢棄物與事業廢棄物的焚化。在甲烷方面,由於未來20年廢棄物的焚化比例不斷上升,將使得一般廢棄物與事業廢棄物掩埋排放產生的甲烷數量在未來的20年內逐年下降;另一方面,我國農業活動將出現逐年下降的趨勢,也使得農業部門排放出的甲烷數量逐年遞減。在氧化亞氮方面,雖然農業活動將出現逐年下降的趨勢,使得農業排放的氧化亞氮逐年減少,但由於交通工具排放出的氧化亞氮將逐年上升,且上升的幅度較農業部門減少的幅度大,導致氧化亞氮的排放在未來20年間將出現緩慢上升的趨勢。
在模型的比較方面,TAIGEM-D模型引用適當的經濟理論,建立聯立方程組來描述經濟體系的行為,屬於結構式模型中的一種。結構方程式的解經過適當的轉換,可以縮減式表示,也就是說,只要給定一組外生變數、前期內生變數的值與殘差項的值,透過縮減式就可以解出本期的內生變數,並可透過系統的動態性,求得未來的預測值,故本研究設定的狀態空間模型可視為TAIGEM-D模型的縮減式。根據第五章的實證結果可知,兩者預測的溫室氣體排放基線相當接近,但基線的排放結構卻有所差異。雖然狀態空間模型與TAIGEM-D均使用逐期(年)動態的機制來進行預測,但狀態空間模型利用長時間的歷史趨勢並藉由觀測誤差值極小的觀念進行預測,其預測結果將表現出各能源的趨勢變化。於第三章模型探討時,我們認為狀態空間模型預測結果的變動幅度將明顯地比透過資本存量變動的TAIGEM-D為激烈,本研究第五章的實證結果支持這種看法。
After Kyoto Protocol had established, we find that accurate estimation and forecasting of baseline greenhouse gases emissions will facilitate the understanding of environmental change and lay a foundation for international cooperation to reduce greenhouse gases emissions. Following a thorough examination of the meaning of baseline and its related issues, we chose the energy, agriculture, waste, and other sectors as target sectors to calculate the emissions of CO2, CH4, N2O for them from years 1986 to 2000. Besides, we use both State Space and TAIGEM-D Models to forecast the emissions for the same sectors into the future and compare the results based on methodological issues.
As for the baseline greenhouse gases emissions in Taiwan, we analyze the energy, agriculture, waste, industrial waste, transportation sectors individually and examine the leakage from the energy sector. We found that the energy sector had highest emissions in the past fifteen years(1986-2000) in Taiwan, which is about 55% to 75% of total emissions, and the ratio is rising year by year. The industrial waste and waste sectors are the next two highest emitting sectors, accounting for about 25% to 40% of total emissions, but the ratio is declining every year. The forecasts of Taiwan’s baseline greenhouse gases emissions in the next twenty years(2001-2020) has a structure very similar to what it was before. However, the energy sector not only accounts for the most part of the emissions, but also with a rising ratio. Therefore, if our government can work out a good energy policy on demand management to restrain the consumption of carbon—containing energy, it will slow down the rising greenhouse gases emissions in Taiwan.
The emissions of CO2, CH4, and N2O from our baseline estimates for Taiwan shows that CO2 is the highest greenhouse gas emitted in the past fifteen years, which accounts for about 55% to 75% of all emissions, and the ratio is rising year by year. CH4 emissions accounts for about 25% to 40% of total emissions, but the ratio is declining every year. Because of the increase in burning waste and industrial waste in the next twenty years, CH4 emissions resulting from the buried wastes and industrial wastes will decrease every year. On the other hand, it has been shown that there will be a steady decrease in agricultural activities year by year, so there is going to be a reduction in the CH4 emissions from the agriculture sector.
TAIGEM-D, which is one kind of structural models, quoted appropriate economic theories to set up the simultaneous equations to describe economic activities. The solution of structural equations via proper conversion could be given as reduced equations. It means that if we give a subset of exogenous variables, endogenous variables value of last stage, and value of residuals, we can get the endogenous variables of this stage by reduced equations and get forecast values in the future by the dynamic performance of the system. Consequently, we can take the state space model set up by this study as the reduced equations of TAIGEM-D model. The empirical results in chapter five reveal that the baseline forecasts of greenhouse gases emissions by the state space model approaches the one of TAIGEM-D model, but they are different in the structure of baseline emissions. Although both the state space model and TAIGEM-D model forecast by using dynamic systems year by year, the state space model make use of long-term historical trends and forecast by the concept of minimal error. For this reason, its forecasting results will show the trend changes of all kinds of energy. We think that the change range of the state space model forecasts is wider than the one of TAIGEM-D by capital stocks in chapter three. Therefore, the empirical results in chapter five of this study support the view.
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