Preliminary Study on Valuation of Environmental Improvement in Beijing、北京市能源现状
Preliminary Study on Valuation of Environmental Improvement in Beijing
（未经编辑，请勿引用）
Abstract: The analysis on two indicators of OGGI are undertaken in order to estimate the value of the improvement of environmental quality in Beijing through the residents' willingness to pay (WTP) by using the Contingent Valuation Method (CVM). In this paper, the WTP theory, the research framework, and statistical technologies and models for our study are systematically explained. We identified several factors which seem to have significant influence on WTP, including age, sex, personal income, educational experience et al, and worked out the estimation of annual value of environmental improvement in Beijing. Some results are shown for discussion and for further study.
Key Words: Contingent Valuation Methods; Willingness to Pay; Crosstab Analysis; Correlation Analysis; Regression Model
1 Background
1.1 Objects of environmental improvement in Beijing for Green Olympics
Despite the great progress made in recent years in Beijing’s environmental protection, there is still much for us to do to reach the requirement of a green Olympic Games, owing much to the restrictions in natural geography and climate conditions and the environmental pollution caused by human activities. Especially, the air pollution in the urban area is still serious, while the water is far below the national water quality standard. In Beijing Olympic Action Plan, a sub-plan of Environmental Protection is made on the basis of the Objectives and Tasks listed by the Action Plan, aiming at realizing Green Olympics, enhancing sustainability of the city and ensuring the 2008 Olympics to create rich and unique environmental heritage for Beijing, China and the Olympic movements (BOCGO, 2001). There are respectively objects for both air and water as major indicators of environmental improvement:
Ambient air quality: By 2008, the main air pollutants should meet the national standards based on the remarkable regional ecosystem improvement. During the Olympic period in 2008, concentration of SO2, NO2 and O3 in urban area should reach the WHO guidelines and the particles should be comparable to that in the major cities in the developed countries.
Water environment: By 2008, the water quality of Miyun and Huairou reservoir should keep meeting national standards, Guanting reservoir should basiclly resume the function of drinking water source; tap water quality should continue to meet WHO guidelines. The sewerage treatment rate (secondary treatment) in the urban area and the satellite towns should be increased to 90%, and the reclamation rate should come close to 50%.
1.2 valuation of environmental improvement for OGGI
In original OGGI indicator system, there are mainly physical or chemical indicators for estimating the environmental impact of Olympic Games. However, as there is huge investment in environmental protection and pollution control during the period of preparing and holding Olympic Games, we consider it necessary to include the indicators of economic valuation of environmental improvement, so that we can have the global environmental impact of Olympic Games assessed. That is why we decide to introduce additional indicators in OGGI’s: i) En44: Economic Value of Water Quality Improvement; ii) En45: Economic Value of Air Quality Improvement. The analysis on two indicators of OGGI are undertaken through the residents' willingness to pay (WTP) by using the Contingent Valuation Method (CVM).
Before we turn to consider the detail of this study, we wish to emphasize that we are addressing only certain of the issues raised by use of the CVM and recognize that such an analysis may not of itself provide a sufficient basis for validation of the method.
2 Evaluate monetary value for environmental goods
In the view of environmental economics, environmental goods should have measurable, monetary value, for any change in their quantity or quality can affect the consumers’ utility or welfare. However, it seems to be difficult to measure the value of environmental change because most environmental goods have not got market prices till now. The only proper way to account the value is to search for the consumers’ willingness to pay (Mitchell，1989). The contingent valuation method (CVM) is a survey-based technique for eliciting preferences to evaluate willingness to pay for generally non-market goods, in a form which allows one to estimate how survey respondents trade-off private consumption for a non-market good in monetary terms. It has been the most commonly used approach to place a monetary value on environmental resources (Mario et al., 1995; Loom et al., 1997; Carson, 1998).
Given the individual’s minimum expenditure function is:
(3)
Where e(*) is the expenditure model that refers to the minimum expenditure with given utility; P is the price vector of a set of commodity; and Q is the amount vector of the commodity set; x 0 and x1 are separately the quantity or quality of environmental goods consumed before and after the environmental improvement occurs; in that case, the individual’s maximum WTP is the difference between the expenditure function with x 0 and that with x 0, as can be seen from Eq. (2):
(4)
Then we can calculate the monetary value of environmental improvement by summing up individuals’ WTP:
Value= total WTP= = (3)
Where refers to the mean calculated by averaging a set of WTPs from the survey; n is the total population of the area.
3 Framework of our study
In this study, we assume that there should be obvious improvement of both air and water quality in Beijing from the present day to 2008, so that the monetary value of environmental resources will increase as a result. Therefore, we use ∆x to denote the environmental improvement, while ∆y denotes the increment of value. ∆x is a certain amount depended on baseline quality x0 and target quality x1 that is established in government plans and is to be realized by relevant policies and measures. ∆y is unknown and supposed to be estimated by CVM survey in which we can find out the residents' WTP. According to WTP theory, individual’s WTP is dependent on his expenditure function: . As far as is concerned, however, in this study, three issues of major factors should be highlighted: firstly, ∆x is a constant; secondly, P refers to the cost of environmental improvement which depends on the investment in environmental protection and pollution, given that prices of other goods are constant; thirdly, what is most complex, individual’s utility function is dependent on a series of factors including those of economic, social, physical and psychological aspects, such as income, education, consumption preference, et al.. In order to identify these factors, we designed a series of questions useful information included in CVM questionnaire. The basic framework of our study can bee seen from Fig. 1:
*Price refers to the price of environmental goods, i.e. the cost of environmental improvement, given that prices of other goods are constant.
Figure 1 Study Framework
4 Preliminary results
In total 2225 interviews have been successfully completed in our CVM survey, consisting of 1975 in urban areas and 250 in Baihe Basin in Beijing. Then three steps for data analysis are mainly planned to be carried out: (i) descriptive statistic for useful variables; (ii) correlation analysis, in order to find out correlation of variables, especially that between individuals’ WTP and their economic, social characteristics such as income, education, awareness of environmental protection et al.; (iii) regression models based on results of correlation analysis.
4.1 Results of WTP
In our CVM survey, residents were asked 3 types of WTP questions: (i) “will/won’t” pay; (ii) maximum amount of payment by cash; (iii) maximum amount of payment deduct from the wage. 38.8% of residents answered “yes” to the “will/won’t pay for improvement for air quality” question, while 35.6% will pay for improvement for water quality. Of all those residents, the maximum payment by cash is 1000 yuan per month for either air or water quality improvement, and the minimum payments are separately 1 yuan per month. As a result, the average WTP for air quality improvement of all 2225 residents is 7.79 yuan per month, and that for water quality improvement is 7.07 yuan per month. More results of WTP can be seen from Table 1 and Table 2,
Table 1 will / won’t pay for environmental improvement
air quality water quality
frequency percent % frequency percent %
won’t 1358 61.0 1426 64.1
will 863 38.8 793 35.6
no reply 4 0.2 6 0.3
total 2225 100.0 2225 100.0
Table 2 WTP in different forms
air quality water quality
cash(yuan per month) wage(%) cash(yuan per month) wage(%)
N 2225 2225 2225 2225
Mean 7.79 0.46 7.07 0.41
Std. Deviation 36.174 1.276 35.713 1.123
Minimuma 1 1 1 1
Maximum 1000 30 1000 20
Sum 17327 1028 15736 910
a Minimum bid = zero throughout
4.2 correlation analysis
Correlation analysis is planned to be undertaken in our work so as to study the correlation of WTP and other economic, social factors. Following are two demonstrations for discussion: first, the null hypothesis H0 is that ratios “will pay” to “won’t pay” of residents of different background are coincident. We use crosstab analysis to test the hypothesis:
Table3 “will / won’t pay” * background _crosstab output
will/ won’t pay_air will/ won’t pay_water
Pearson Chi-Square Asymp. Sig. (2-sided) Pearson Chi-Square Asymp. Sig. (2-sided)
Demography sex 2.24 0.326 3.25 0.197
location 105.48 0.000 116.28 0.000
commodity type 42.83 0.000 44.29 0.000
education educational experience 92.19 0.000 109.02 0.000
occupation vocation 103.62 0.000 93.16 0.000
position 47.92 0.000 32.62 0.000
health personal health 34.15 0.000 33.60 0.000
public awareness Olympic 25.06 0.000 24.93 0.000
environmental protection 41.64 0.000 49.83 0.000
environmental information air pollution 16.95 0.009 0.000
water pollution 0.000 31.44 0.000
information acknowledged 40.65 0.000 37.99 0.000
The outputs of crosstab analysis are listed in Table 3. All c2 test statistic are significant except variable of sex. We can refuse H0, and conclude that residents of different background behave obviously differently in replying to the “will / won’t pay” question, i.e., factors such as location, commodity type, educational experience and occupation have influence on residents’ decision of “will / won’t pay”.
In order to test the traditional argument that the residents’ WTP is largely dependent on their total income, we calculated the Pearson Correlation coefficients of WTP and corresponding income by SPSS. The correlations are mostly lower than 0.1 and not significant at the 99% level.
Table4 Correlations: WTP& Income
annual income of household annual personal income
WTP_air cash Pearson Correlation 0.060 0.010
Sig. 0.093 0.785
wage Pearson Correlation 0.068* 0.010
Sig. 0.002 0.644
WTP_water cash Pearson Correlation 0.063* 0.020
Sig. 0.004 0.357
wage Pearson Correlation 0.093* 0.011
Sig. 0.000 0.625
*. Correlation is significant at the 0.01 level (2-tailed).
4.3 models
Based on the results of correlation analysis, we can abstract useful variables for regression models. There may be also three types of dependent variables available: (i) “will/won’t” pay; (ii) payment by cash; (iii) payment by wage reduction (seen from Table 5). Independent variables should include social, economic, demographic variables and others about environmental information. We must emphasize that all variables listed in following tables are just optional not necessary and need to be further filtrated in modeling.
Table 5 dependent variable
name definition property
AWIL / WWIL will /won’t pay dichotomous, dummy dependent1=will，0=won’t
AWTP / WWTP payment by cash
AWAGE / WWAGE payment by wage reduction
Table 6 independent variable (optional)
name property
C constant
SEX dichotomous, dummy variable
AGE
LOCi location-the district the resident’s living dummy variable
TYPEi community type dummy variable
OCi occupation dummy variable
PINC annual personal incomeFINC annual family income
EDUi education dummy variable
ENVPAi participation in environmental protection activity dummy variable
RESPi self-responsibility for environmental protection dummy variable
INFO i acknowledged environmental information dummy variable
APOL i feeling of air pollution in Beijing dummy variable
WPOL i feeling of water pollution in Beijing dummy variable
WWTF i agree to rise water rate for collecting fund for waste water treatment
AIRi satisfaction with air quality in Beijing dummy variable
WATER i satisfaction with water quality in Beijing dummy variable
4.3.1 Linear probability models (LPM)
The LPM is given as Eq. 4:
Yi= a0+a1D1i+……+akD ki + b1X1i+……+bjXji +u
=a0+åakDki +åbmXmi+u (4)
Where the dependent is
if the resident will pay;
if the resident won’t.
and two types of independents: D- dummy variables，X-quantitive variables.
Providing estimates of are unbiased, i.e., E(ui)=0，we defined Pi as the probability of “Yi=1” occurring (will pay), so 1- Pi is the probability of “Yi=0” occurring (won’t pay). Then there is:
E(Yi|Di, Xi)= a0+a1D1i+……+akD ki + b1X1i+……+bjXji = Pi (5)
Although u in LPM is not normally distributed, we decide to use OLS for estimation because of “large sample law”(Gujarati, 2000). The regression coefficient of each variable implies the change of conditional probability of “will pay” occurring due to a unit change of the variable.
Table7 LPM output
Dpendent
will/ won’t pay for air quality improvement will/ won’t pay for water quality improvement
Independent coefficients
Constant -0.184 -0.187
Location Dongcheng District 0.031 0.049
Xicheng District 0.064 0.063
Chongwen District -0.060 -0.035
Xuanwu District 0.016 0.033
Chaoyang District 0.092 0.105
Fengtai District -0.101 -0.089
Shijingshan District -0.035 0.013
Haidian District 0.139* 0.185**
Shunyi District -0.041 0.012
Huairou District 0.060 0.085
Educationalexperience elementary school and below 0.124 0.106
junior high school 0.080 0.083
senior high school 0.134 0.130
Vocational school 0.175 0.123
Specialized secondary schools 0.133 0.113
College for Professional Training（part-time） 0.235** 0.259**
College for Professional Training（full-time） 0.258** 0.264**
undergraduate（part-time） 0.267** 0.182
undergraduate（full-time） 0.225** 0.215**
postgraduate and above 0.319** 0.357***
vocation principal of enterprises, institutions or organizations 0.091 0.073
technician 0.178 0.096
clerk 0.185 0.145
services 0.131 0.144
workers in farming, forestry, animal husbandry and fishery 0.046 0.064
manufacturer 0.166 0.119
other worker 0.059 0.061
student 0.260 0.235
unemployee/ laid-off 0.083 0.062
retired 0.131 0.105
other unemployee 0.034 0.012
professional -0.094 -0.068
manager 0.597 0.616
environmental information acknowledged full 0.288* 0.275*
mostly 0.236* 0.232*
some 0.201 0.169
little 0.343** 0.265*
income annual income of household 3.320e-007 3.703e-007
annual personal income 8.681e-007 5.693e-007
R2 0.076 0.078
adjusted R2 0.057 0.060
F 4.021 4.179
“*”, ” **”, ” ***”: significant at 10％, 5％, 1％ level.
Seen from Tab. 7, a few variables are significant, such as “living in Haidian District”, “educational experience of or above college” and “environmental information acknowledged”. For example, given other variables unchanged, residents living in Haidian District showed a 0.18 higher probability of “willing to pay” than residents living in other districts. Similarly, residents with educational experience of or above college also showed higher probability “willing to pay” than others. Residents’ environmental information acknowledged has significant influence on their decision of “willing/ won’t to pay”. However, income is not a significant influence factor on the probability of “willing to pay”.
4.3.2 Linear regression model (LRM)
Assuming the general function of WTP as following:
(11)
Where the dependent is amount of monetary WTP, and independents include variables of population, location, education and so on.
Table8 LRM output
Dependents
WTP for air quality improvement WTP for water quality improvement
Independent coefficients
Constant 9.051 9.155
Demography sex -0.455 -0.748
age -0.190** -0.192**
Location Dongcheng District -1.683 -0.911
Xicheng District 2.191 0.688
Chongwen District -0.742 0.202
Xuanwu District 2.091 1.466
Chaoyang District -1.784 -1.063
Fengtai District -3.018 -2.702
Shijingshan District 6.986 8.881
Haidian District 7.461 8.413*
Shunyi District 0.002 0.917
Huairou District 0.112 0.111
Position administrant -0.186 0.093
Educationalexperience elementary school and below 0.791 -0.204
junior high school -2.508 -2.696
senior high school -2.295 -2.513
Vocational school -1.499 -4.516
Specialized secondary schools -3.180 -3.410
College for Professional Training（part-time） 2.106 1.930
College for Professional Training（full-time） 6.364 5.362
undergraduate（part-time） 2.376 -3.821
undergraduate（full-time） -2.157 -2.506
postgraduate and above 38.154*** 38.168***
environmental information acknowledged full 3.025 -0.864
mostly 4.505 3.960
some 2.561 2.326
little 2.897 1.917
income annual income of household 5.070e-006 4.738e-006
annual personal income 1.591 e-004*** 1.325e-004***
R2 0.061 0.061
adjusted R2 0.046 0.045
F 3.962 3.909
Durbin-Watson 1.987 1.981
“*”, ” **”, ” ***”: significant at 10％, 5％, 1％ level.
Seen from Tab.8, variables including “sex”, “annual personal income”, “living in Haidian District” and “educational experience of or above postgraduate” are significant. The coefficient of variable “sex” is negative, which imply that the older has a lower WTP in total. Given other variables unchanged, residents with educational experience of or above postgraduate will pay an average amount of over 38 yuan more than others, which is of great significance. Furthermore, “annual personal income”is a significant influence factor on WTP, while “annual income of household”is not. It is also proved by their scatter table that there is probably linear correlation between “annual personal income” and WTP.
Table2 Scatter of WTP- Annual Personal Income
5 Value of environmental improvement in Beijing
The annual value of air quality improvement in Beijing is 38297.3 yuan, calculated by equation (3):
Value (Air)= = =7.79 12 409.6844=38297.3
where refers to the mean of WTP from our CVM survey, and n is the number of family households in Beijing in 2003. Similarly, the annual value of water quality improvement in Beijing is 34757.62 yuan.
We can also have another result of that value by another equation:
WTP=exp( ),
when we have estimated the coefficients b of proper regression models.
6. Summary
This analysis was undertaken in order to estimate the value of environmental improvement through the residents' willingness to pay (WTP) by using the Contingent Valuation Method (CVM). According to our recent survey, a preliminary results show that the annual value of the improvement of air quality in Beijing is 382,973,000 yuan , and that of water quality is 347,576,200 yuan.
We also identified several factors which have significant influence on WTP:
First, although income is said to be the major influence factor of WTP, we found that income annual personal income is more significant than total income of household.
Second, educational experience has significant positive influence on WTP. Residents of higher educational degree seem to be more willing to pay for environmental improvement, as well as to show higher payment.
Moreover, it is indicated that age is somewhat significantly a negative influence factor; and interestingly, residents living in Haidian District showed both bigger probability of willing to pay and higher payment than other residents. What is the reason has not be found out yet.
The task for future research must be to examine and to refine the relation between WTPs and their influencing factors. Furthermore, we ought to have the creditability of the data analysis tested after it completes. As a stated preference method, CVM has inherently got some errors of the estimate. Therefore, it will be helpful to have the value calculated by WTP compared with that by some other valuation method.
References
8. Thomas C. Brown, Robin Gregory, SURVEY:Why the WTA-WTP disparity matters, Ecological Economics 28 (1999) 323-335
9. Richard T. Carson, Analysis: Valuation of tropical rainforests: philosophical and practical issues in the use of contingent valuation, Ecological Economics 24 (1998) 15-29
10. J. B. Loomis, Walsh R. G., Recreation Economic Decisions, Comparing Benefits and Costs (second edition). Venture Publishing Inc, Pennsylvania (1997).
11. R. C. Mitchell and R. T. Carson, “Using Surveys to Value Public Goods: The Contingent Valuation Method,” Resource for the Future, Washington, D.C. (1989).
12. A. Myrick Freeman III, The Measurement of Environmental and Resources Values, Resources for the Future, Washington, D.C., (1993).
13. Marion Niklitschek and Javier Leon, Combining Intended Demand and Yes/No Responses in the Estimation of Contingent Valuation Models, Journal of Environmental Economics and Management 31, 387-402 (1996).
14. Damodar N. Gujarati, Basic Econometris，China Renmin University Press，538-555(2000).
15. Beijing Organizing Committee for the Games of the XXIX Olympiad, Olympic Action Plan, http://www.beijing-olympic.org.cn/43/44/column211614443.shtml
北京市能源现状
摘 要 为了奥运会的成功举办，北京市在吸取以前各届奥运会成功的宝贵经验基础上，正在加快以场馆和交通设施建设为主的奥运会筹备工作。而评价在北京举办奥运会对北京乃至中国的经济、社会—文化以及环境的整体影响，则是一个前无古人的壮举。能源的利用不仅贯穿整个社会的发展过程，而且对环境产生巨大影响。本文在这样的背景下，描述了北京市能源的现状，以期为评价奥运会的举办对北京和整个中国能源生产和消费产生的影响，进而为评价对环境产生的影响建立基础。
关键词 能源，能源消费，现状，北京
Abstract: Beijing is speeding up the work to prepare for the Olympic Games to be hold in Beijing in 2008, on the basis of the former Olympic Games’ experiences, which mainly contain stadiums and traffic establishment construction. It is an unprecedented feat to assess impacts of Olympics to the economy, social culture and environment of Beijing and even China. Energy consumption not only impenetrate the development course of society, but also affects the environment tremendously. On this background, this article describes the energy states in Beijing, to build up the basis to assess impacts of Olympics to energy production and consumption in Beijing and whole China, sequentially to assess impacts to environment.
Keywords: Energy, Energy Consumption, Current States, Beijing
一． 北京市能源生产与供应
北京地区的能源资源极为有限, 能源供应以外地调入为主，自产煤炭主要是无烟煤，分布在京西门头沟和房山区，有少量的水力发电资源(到目前为止已开发利用近一半)，石油和天然气尚未发现可供开采的工业储量。电力供应60%从华北电网调入；天然气来自陕甘宁长庆天然气气田和华北油田，1998年调入量为3.8亿立方米，2000年调入量为11亿立方米，2002年调入18亿立方米。原油也是全部由外地调入；原煤主要由山西调入，2000年共调入1902万吨标准煤，大大超过本地产量492万吨标准煤。
虽然北京的一次能源大部分依靠外地调入，但北京拥有相当大规模的能源加工转换工业，能源加工转换主要是燃煤发电、供热、焦化厂和首钢的炼焦以及燕化的炼油, 每年都有不少二次能源(如成品油)调出北京。
二． 北京市能源消费状况
2000年北京市能源总消费量为4110万吨标准煤，仅次于上海，居全国第二位，与1995年相比增加了500多万吨标准煤，从1995年至2000年，平均年增长率为2.6%。2000年人均能耗3.70吨标煤，2000年北京GDP产值2478.76亿元，单位GDP产值能耗为1.65吨标准煤/万元(如表1)，高于全国平均水平的1.56吨标准煤/万元。
表1 北京市2000年能源总消费量及能耗水平
能源总量（万吨标煤） 人均能耗（TCE） GDP产值能耗（TCE/万元）
4110 3.70 1.65
注: GDP数据来源于北京市统计年鉴
北京市2000年终端能源消费品种繁多，包括煤炭、焦炭、天然气、液化石油气、燃料气、煤气、电力、热力、油品及地热能、太阳能等可再生能源。构成情况如表2所示，一次能源消费情况如表3所示。
表2 北京市2000年终端能源消费品种构成
消费量(万吨标煤) 比重
一、固体能源 1331 35.90
煤 898 24.22
焦炭 433 11.68
二、液体能源 735 19.82
燃料油 161 4.34
汽煤柴油 273 7.36
其它油品 275 7.42
焦化产品 26 0.7
三、气体能源 256 6.90
天然气 95 2.56
液化石油气 48 1.29
煤气 87 2.35
燃料气 26 0.7
四、热力 185 4.99
五、电力 1201 32.39
总计 3708 100
注: 数据来源于北京市发展计划委员会奥运行动规划-能源建设和结构调整规划报告
表3 北京市2000年一次能源消费构成
单位 煤 油 天然气 电力 合计
万吨标准煤 2111 1004 146 849 4110
比重% 51.4 24.4 3.6 20.6 100
注1: 天然气和电力数据来源于北京市发展计划委员会奥运行动规划-能源建设和结构调整规划报告
注2: 煤炭和油的数据来源于北京市统计年鉴
北京市能源消费结构中煤占了较大比例。在终端能源消费结构中，煤炭和焦炭所占的比重高，占据主导地位，不仅如此，还有大量的发电、供热和炼焦用煤，终端能源消费中煤炭的比重为36%，一次能源消费中煤的比重为51.4%，且煤的利用技术水平低。
以煤炭为主的能源结构与首都形象和社会经济发展不相适应。长期以来，受国家资源和能源政策的影响，北京市的能源结构中，煤炭一直占据主导地位。以煤为主的能源结构，是造成大气严重污染的根源之一。目前北京是世界上大气污染最严重的十个大城市之一，这很不符合北京的城市功能和性质，更影响了我国在国际上的形象。表4给出了1990-2000年煤和焦炭的终端消费量。
表4 北京市1990-2000年煤炭和焦炭的终端消费量（万吨标煤）
1990 1995 2000
消费量 比重(%) 消费量 比重(%) 消费量 比重(%)
煤 985 38.9 995 31.1 898 24.22
焦炭 216 8.5 476 14.9 433 11.68
终端消费总量 2534 100 3196 100 3708 100
注: 数据来源于北京市发展计划委员会奥运行动规划-能源建设和结构调整规划报告
可以看出，煤和焦炭在终端能源的比例长期居高不下，以煤炭为主的能源结构，与人民生活水平提高不相适应。目前，北京人民生活水平已初步实现了小康，本世纪将逐步向富裕迈进，在人们物质生活需要得到满足的情况下，对居住与生活环境提出了较高的要求。如果不从根本上改善能源结构，将难以满足居民对提高生活质量的要求。
近几年来,北京市加快了能源结构调整的步伐，随着能源结构调整的进行，电力、天然气占的比例将逐年提高。表5给出了1990-2000年天然气和电力在终端能源消费量中的比例，长远目标应实现终端能源的绿色化。
表5 北京市1990-2000年天然气和电力的终端消费量（万吨标煤）
1990 1995 2000
消费量 比重(%) 消费量 比重(%) 消费量 比重(%)
天然气 11 0.4 13 0.4 95 2.56
电力 552 21.8 779 24.4 1201 32.39
终端消费总量 2534 100 3196 100 3708 100
注: 数据来源于北京市发展计划委员会奥运行动规划-能源建设和结构调整规划报告
表6 北京市2000年各产业能源消耗与国内生产总值贡献比较（万吨标煤）
第一产业 第二产业 第三产业 总计
国内生产总值(亿元) 89.97 943.51 1445.28 2478.76
各产业国内生产总值比例 3.63% 38.06% 58.31% 100%
能源消耗 104.9 2264.7 804.9 3174.5
各产业能源消耗比例 3.3% 71.35% 25.35% 100%
注: 能源数据来源于北京市发展计划委员会奥运行动规划-能源建设和结构调整规划报告
北京市第二产业GDP贡献率与能源消耗比例极不相称，第二产业GDP贡献率为38%，但其能耗却占到了各产业能源消耗总量的71.35%，与发达国家的30%-40%相比，明显偏高。第三产业GDP贡献率高达58.31%，但其能耗仅占各产业能源消耗总量的25.35%，因此北京市进行产业结构调整，发展第三产业，也是降低单位GDP产值能耗所必须的。具体数据见表6。
产业和产品结构的调整，将对优质能源的需求越来越大，而以煤为主的能源结构，在一定程度上将妨碍产业、产品结构的调整。
和国内平均水平相比，北京的能源消费量高，经济发达，人均生活水平较高，人均能耗高于全国平均水平， 表7给出了北京市1999年人均GDP及能耗与国内外平均水平的比较。
表7 北京市1999年人均GDP及能耗比较
国家或城市 全国平均 OECD国家 北京
人均GDP(US$) 882 23688 2420
人均能耗 (TCE) 1.14 6.97 3.23
但北京的能源消费状况与城市功能、性质、发展目标及可持续发展战略还很不适应。与发达国家相比也存在较大差距，仅相当于OECD国家七十年代的水平。从产值能耗来看，目前万元国内生产总值和万元工业增加值的能耗都高于上海等城市及全国平均值，与国际先进水平相比差距更大，因此北京市能源利用水平尚有待提高，节能还有很大潜力。
三． 能源供应及消费的特点及存在的问题
（一）能源供给对外依赖度大
北京市新能源和可再生能源消费量仅占能源总消费量的1.27%，目前北京市每年用煤量高达2700万吨，其中94%都依靠外埠供给，新能源和可再生能源开发利用的现状还不能令人满意，石油和天然气则完全依赖外地调入。能源供应的市场化程度不够，市场竞争机制尚未形成，符合市场经济要求的能源供应体系有待进一步培育和发展。
（二）能源利用效率低
目前北京市能源消费环节浪费比较严重，主要表现在燃煤锅炉热效率较低、建筑采暖热能浪费严重、电机综合效率低、照明用电浪费比较普遍等。发达国家的能源综合利用效率达到40%以上，而北京仅略高于30%；与发达国家相比，北京市工业窑炉平均效率低10%以上，工业锅炉低15-20%，火电厂平均煤耗高20%，城镇居民生活燃煤热效率平均仅为22%左右。北京目前建筑综合热指标平均在55大卡/平方米.时，而按照北京市有关建筑节能的规定，新建居住建筑热指标可降低到40大卡/平方米.时左右；电动机采用变频调速(发达国家一般均采用调速设备，北京在用的变频调速设备不多)以后，电耗一般可节约20-30%左右；首钢综合吨钢能耗在0.97吨标煤，而目前国际先进水平在0.65—0.7吨标煤。另外在市区由于替煤而改造或新建的燃气锅炉（效率一般为55%）能源利用效率还有很大的提高潜力，如果改造为热电冷联供对能量实现阶梯利用，则产热效率可以达到85%，污染物排放也可以进一步降低。
（三）煤的利用主要是直接消费
北京市煤的利用中直接消费是最大一部分，主要是通过23000多台工业锅炉、工业窑炉锅炉和民用生活取暖炉来使用。燃用未经洗选的散煤，大多烟筒低、无脱硫、脱氮措施，除尘效率不高。
（四）洁净煤技术利用少
目前CCT技术的应用还非常有限,洗选煤的市场很小，型煤仅仅在民用方面得到应用，尚未应用于工业锅炉，水煤浆技术目前仅在燕山石化的220吨/小时的锅炉上得到成功应用，其他如大型的循环流化床和IGCC技术目前均未得到应用。对于北京来说，未来若干年内还将使用相当数量的煤，因此应该重视开发与利用清洁燃烧技术、燃煤污染控制技术及洁净煤技术。清洁燃烧技术有循环流化床燃烧技术、整体式煤气化联合循环(IGCC)技术等，主要用于发电厂。研究使用这些技术的可行性，在条件成熟、技术成熟、时机成熟之后，研究在远郊区县建设利用清洁燃烧技术的发电厂的可行性。成熟的燃煤污染控制技术主要应用于发电厂，包括脱硫、脱氮工艺或低氮燃烧。对于目前北京在用的燃煤发电厂，需要在2005年以前全部改造并使用脱硫、脱氮工艺，努力降低二氧化硫和氮氧化物的排放总量。
（五）天然气价格偏高
天然气替煤作为北京市能源结构调整的一个重点方向，目前发展势头良好，但也存在很多障碍。2000年北京市利用天然气11亿立方米，2002年估计将利用天然气18亿立方米，预计2005年将消耗天然气40亿立方米。但目前存在的问题是价格过高，因此局限了它的使用，目前北京市天然气利用主要集中在居民使用和市区采暖，其他利用方式较少，造成了峰谷差值相当大，燃气集团在未来几年天然气的消耗去向压力很大。此外，目前天然气的利用主要采用直接燃烧的方式，利用效率偏低，北京市正在积极进行天然气热电冷联供的示范工程建设。
（六）新能源及可再生能源利用比例较少
近年来，北京市在新能源和可再生能源方面发展较快，但总起来说新能源和可再生能源的使用严重不足，与北京市的发展不相称。目前在北京市发展较快的新能源和可再生能源主要有地热利用和太阳能利用等。作为高效的能源利用技术，热泵技术得到了快速的发展。家用空调器的空气源热泵主要用于过渡季节采暖，地源和水源热泵技术主要用于建筑采暖和热水供应。2000年底，共开发地热井200多口用于供热、洗浴，年开采热水900万吨，地热供热面积58.83万平方米；太阳能的主要利用形式为太阳能热水器，另外还有少量的太阳能光伏发电系统；此外，北京市目前水电总装机容量约90万千瓦。只有很少的风电，没有购买外地的风电；生物质能资源目前主要是利用养殖业产生的粪便、生活垃圾等产生的沼气。总体来说，新能源和可再生能源在能源结构中的比例很小，还有很大的发展空间。
四． 总结
北京市能源生产不能自给，过于依赖外地调入。同时，北京是个高耗能城市，能源主要用于工业生产，以煤为主的能源结构，温室气体排放量大，烟尘污染严重，以及能源利用效率低下是目前北京市能源状况面临的最主要问题。目前，北京市迫切需要改变能源消费结构，减少用煤，增加新能源和可再生能源利用率。北京有较丰富的地热、太阳能、生物质能和风能资源，只要上规模，保证必要的投入，从技术、经济的角度都是切实可行的。开展地热、太阳能、生物质能和风能等新能源和可再生能源的建设项目，只要积极开展对外合作，容易引进资金、引进技术，获得国际援助。这样必然生长出一大批新兴的高新技术产业，有助于北京市的产业结构调整，扩大就业机会。另外，在能源政策方面，还有相当一部分政策是在以煤为主的能源结构条件下制定的，与目前环保目标相配套的能源政策不够完善，特别是缺乏对煤炭消费的强有力的抑制政策和对优质能源消费的鼓励政策，难以满足新形势的需要。而且，能源供应的市场化程度不够，市场竞争机制尚未形成，符合市场经济要求的能源供应体系有待进一步培育和发展。
参考文献：
1．北京晨报 2004-8-14
2．通网·一网通天下 http://www.allnet.cn
3．北京市统计信息网 http://www.bjstats.gov.cn
4．中国可再生能源信息网 http://www.crein.org.cn