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題名：中國工業部門的能源和減排效率分析
作者：滕翔宇
作者(外文)：TENG, XIANG-YU
校院名稱：東吳大學
系所名稱：經濟學系
指導教授：邱永和
學位類別：博士
出版日期：2020
主題關鍵詞：中國工業部門；能源效率；減排；資料包絡分析；動態分析；共同邊界法；非意欲產出；China’s industrial sector；Energy Eeficiency；Emission reduction；Data envelopment analysis；Dynamic analysis；Meta-frontier；Undesirable output
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中國是世界上最大的能源消費國，並且存在嚴重的空汙問題。工業部門是中國主要的能源消費和廢氣排放來源。自2010年起，中國政府頒布管制措施，主要針對工業部門加強對能源消費與廢氣排放的要求，並設置了重點區域和“十二五”期間（2011-2015）的減排目標。本研究以分析中國省級工業部門在2011年至2015年期間的能源與減排效率為目的。
資料包絡法（DEA）是進行效率評估的通用方法。當用DEA推估中國的能源效率時，常利用人口、資本和能源消費量作為投入項，將GDP和非意欲排放物作為產出項。然而，少有研究考慮空氣汙染的治理投入，從而忽視了政府為應對空氣汙染而付出的努力。此外，先行研究評估中國能源的表現，通常將能源消費量作為單一的能源相關投入項，這種方法缺乏對能源消費結構進行分析的能力。
首先，我們認為傳統的能源效率分析存在盲點，即忽視了DMU為降低排放而付出的努力。因此，本研究在先前研究的基礎上，將排放治理作為一個新的投入變量，並使用動態SBM模型評估中國工業部門的能源與減排效率。作為結論，我們發現效率的提升不僅來自於能源結構的優化和能源強度的降低，還來自於對排放治理的投入，因為其有效降低了非意欲產出。
其次，我們嘗試將能源投入項拆分為兩個不同的投入項，即煤炭消費與非煤炭消費，並根據他們與其他變量相關性的不同，利用混合動態DEA模型評估中國省級工業部門的能源效率。我們比較煤炭消費與非煤炭消費的改善空間，並得到結論：東部與中部地區的省份應致力於降低煤炭消費從而改善能源效率，而西部地區的大多數省份應在提高能源利用率與降低煤炭消費之間尋求平衡。
最後，由於在重點區域與非重點區域存在區域性的治理差異，因此，我們應用non-radial DDF與共同邊界法推估中國工業部門的能源與減排效率，並指出在重點區域與非重點區域間所存在的，顯著並擴大的技術差距。此外，我們嘗試建立指數，衡量改善能源與減排效率的策略選擇。作為結論，我們發現增加治理費用，對表現的改善更加有效，即使投入會有所增加。這可以作為提升整體能源與減排效率的有效途徑。
China is the world’s largest energy consumer, has the most serious air quality conditions in the world. Industrial sector is the main source of energy consumption and waste gas emissions in China. Since 2010, the Chinese government has strengthened governance requirements mainly affect the industrial sector, setting key regions to establish an emission reduction target of air pollutants during the 12th five year plan (2011–2015). This study is aim to analyze energy and emission reduction efficiencies of China's provincial industrial sector during 2011-2015.
Data Envelopment Analysis (DEA) is a common method of efficiency evaluation. When using the DEA method to assess the energy efficiency of China, population, capital, and energy consumption are usually listed under inputs, while GDP and emissions are classified as outputs. However，there is very little research considering the expenditure treatment on air pollutants and evaluating the improvement effects for the government to manage air pollutants. In addition, previous studies exploring China’s energy performance typically used energy consumption as an input, but this lacks the analytical capacity for energy consumption structure.
First, we believe that there is a blind spot in original energy efficiency analysis, ignoring DMUs’ efforts to reduce emissions. Thus, this study uses emission treatment as a new input on the basis of past literature, and employs the dynamic SBM model to evaluate the energy and emission reduction efficiencies of China’s industrial sector. The study finds that the improvement in industrial sector efficiency is not only due to optimization of the energy consumption structure and reduction of energy intensity, but also from investing in emission treatment methods that help cut emissions as an undesirable output.
Second, we have split the energy input into two different inputs, coal consumption and non-coal energy consumption, and based on their differences
with other variables, the Hybrid Dynamic DEA model was used to assess the energy performance of China’s provincial industrial sector. We then compared coal consumption and non-coal consumption’s room for improvements and have come to the conclusion that provinces in eastern and central China should reduce the amount of coal consumption, thereby improving energy performance. On the other hand, provinces in the western region should attempt to seek a balance between energy utilization efficiency and coal consumption.
Third and finally, there is a different regional treatment of industrial waste gas between key and non-key regions. Thus, this study employs the non-radial directional distance function in the framework of the meta-frontier model to investigate the energy and emission reduction efficiencies of China’s industrial sector and finds a significant and expanded technical gap between key and non-key regions. In addition, this study also looks to establish measurement indices to improve the efficiencies of energy and emission reduction. As a result, this study find out that increasing the input of expenditure, is more effective, and generally leads to performance improvement even with a higher input of expenditure, can play a positive role in the overall upgrading of energy and emission reduction efficiencies.
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