中國電力消費之實證分析

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Title:	中國電力消費之實證分析
Other Titles:	The empirical analysis on electricity consumption in China
Authors:	李柏融;Li, Po-lung
Contributors:	淡江大學中國大陸研究所碩士班陳建甫;Chen, Chien-fu
Keywords:	追蹤資料;中國電力消費;經濟結構;單位GDP電耗;固定效果;Panel Data;Electricity Consumption In China;Fixed effect;Economic Structure,;Unit GDP Electricity Consumption
Date:	2009
Issue Date:	2010-01-10 23:33:15 (UTC+8)
Abstract:	近年來中國快速經濟發展的過程中，引發越來越令人關注的電力供需問題，對於各省份、區域電力的調配及實施的狀況，都是影響未來的經濟能否維持成長很重要的關鍵，而政府及各個產業怎麼面對這個課題，必須針對現況配套因應的政策。本文從電力消費的角度進行量化的分析，首先是關於中國總體電力的發展，接著探討1999年和2006年30個省份（西藏除外）電力消耗的狀況，廣東、江蘇、山東是經濟最發達的省份，同時也是這幾年電力需求最大的；從人均電力消費量的角度來看，上海每個人的電力消耗為最高，但寧夏、青海因為青藏鐵路（使用電能）開通，且觀光客大量的增加，使得人均電力消費量有大幅提昇。而這八年來六大地區電力發展的情況，以長三角經濟區作為代表的華東地區電力消費成長為最快；最後可以看出這八年各省份電能使用的效率有所提昇。本文運用Panel Data模型進行分析，針對中國電力消費與GDP、人口、經濟結構、單位GDP電耗等面向做探討，經過Hausman檢驗出固定效果模型為最適合此資料結構，結果得到GDP越高電力需求越大，工業的比例越高電力的消耗越明顯，以及電能使用效率越差耗電量越高，但在電力消費量取對數後，服務業也會有顯著的影響了，另外人均電力消費量越高其GDP是越高的，而工業化的程度也是越高的。電力的建設跟不上經濟發展的速度，但在經濟發展的壓力下還是要面對電能不足的問題，目前宏觀調控所做的限電、調配等成效有限，而十一五規劃所提倡的節能、產業升級甚至是產業結構的轉換，慢慢可以看出成果。最後提出了相關未來研究建議： (1)完善變量的選擇與量化方法 (2)採用多種函數形式對模型進行更好分析例：1、向量誤差修正模型 2、共整合模型 3、var模型 (3)擴充實證研究中的樣本數據 In the course of China''s fast economic development in recent years, cause the electric supply and demand question attracting people''s attention more and more, the states of the mix and implementation to every province, regional electricity, all the key to influencing future economy could be kept growing up very importanting, and government and each industry how face the subject, must form a complete set to present situation because policy that answers. This text carries on the analysis of quantization in terms of electricity consumption, it is the development about the overall electricity in China at first, then probe into 30 provinces in 1999 and 2006 (except Tibet) The state that the electricity consumes, Guangdong, Jiangsu, Shandong is the most developed province of the economy, it is that the electric demand has been the greatest during these years at the same time; From the point of view of per capita electric volume of consumption, the electricity of everybody in Shanghai is consumed for being highest, but Ningxia, Qinghai because Qinghai-Tibet railway ( Use the electric energy) Open, and tourist''s a large amount of increase, make the per capita electric volume of consumption promote by a wide margin. And the situation of six major region electric power development over these eight years, the electricity consumption of East China which regards long triangular economic zone as representatives is grown into fastest; Can find out the efficiency that every province electric energy use is promoted to some extent these eight years finally. This text use Panel Data model is it analyse, dawdle etc. And face and make the discussion to China between GDP, population, economic structure, cable, unit GDP electricity consumption, to go on, is it happen through Hausman regular result model for suitable for this materials structure most, result get GDP high electric heavy demand to examine, the higher the proportion of industry is, the more obvious the consumption of the electricity is, and the worse electric energy service efficiency is, the higher the power consumption is, but after the electric volume of consumption fetches logarithm, the service trade will have apparent influence, per capita electric volume of consumption high GDP their high in addition, and industrialized degree high too. The construction of the electricity can not catch up with the speed of economic development, but should still face the insufficient question of electric energy under the pressure of economic development, ration the power supply, limited effect of mixing etc. Macro adjustments and controls do at present, and eleven five energy-conservation, industry upgrading, conversion of industrial structure that planning recommend, can find out the achievement slowly. Proposed studying and proposing in the relevant futures finally: (1)Perfect the choice of the variable and quantization method (2)Adopt many kinds of function form to go on more easy to analyse to the model. Example: 1. The vectorial error revises models 2. Combine models altogether 3. Var model (3)Expand the sample data in the positive research
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