Natural Gas Pricing in Residential Sector

Transcription

1 Natural Gas Pricing in Residential Sector Shahla Khaleghi National Iranian Oil Company Corporate Planning Dep. Long Term Plans Affairs : Globalization of Energy: Markets, Technology, and Sustainability 26th IAEE Annual International conference 3-6 June 2005, the Grand Hotel, Taipei Abstract Commodity pricing policy is always affected by economic, political and social factors. Prices determined by considering economic structure such as competition, monopoly in each country. Gas and other commodities are always priced by market-oriented, cost-oriented and governmentcontrolled-oriented methods. Scarcity value and non-renewable characteristics are most important factors which distinguish gas from other commodities. Thus, in natural gas pricing process, this subject should be paid attention. Price discrimination policy in different regions and provinces is very important in natural gas pricing process. Economic and geographic characteristics of each provinces (income per capita, income distribution, and ecological conditions) and economics of gas supply (average cost) are very important factors which affect this process. The aim of this paper is to present a natural gas pricing pattern for residential sector, which till now has been determined base on government-controlled policy. Natural Gas (NG) prices have been subsidized and fixed in all of provinces and there isn't any relation between natural gas pricing and economic & geographic conditions of provinces. This paper presents a new approach for natural gas pricing in Iran. Key words: Pricing, Market-Oriented, Cost-Oriented, Government-Controlled- Oriented, Average Cost, Price Discrimination, Income Distribution, and Scarcity Value 1

2 Natural Gas (NG) pricing pattern in Iran will be too difficult to explain due to its background which has been based almost on political and managerial decisions. To this time, NG pricing pattern has had two especial characteristics: 1. Constant NG price for a long time (till early 1991) and changes in NG prices at political and managerial decisions framework and based on specified growth from early 90 s till now; 2. Independency between gas price and supply marginal or average cost; 3. Non-applied discriminatory pricing pattern based on amount of consumption for a long time (till mid 1990s) and application of this pattern, from mid 1990 s. In latter, gas prices have been explained based on specified bracket of consumption and the range of every bracket was considerable; 4. Non-discriminatory pricing based on provincial differentials; Regarding to above mentioned realities, NG pricing pattern will be too difficult to explain in different consuming sectors and different provinces, because during past decades NG pricing criteria can not be taken in future as a base to describe new and effective pattern. Therefore creation of new thoughts would be inevitable. The main aim of this paper is to present provincial NG pricing pattern in residential sector of Iran. In this pattern, discriminatory pricing policy would be modeled by socio/ economic and political conditions of every province, economics of gas supply for different provinces and the base price of supplying NG. The base price of NG is considered as an exogenous variable and it can be determined by Iranian Economic Council based on social/political decisions or supply average cost by economic surveys. In this paper "value engineering" and "decision theory" (the 2

3 Analytic Hierarchy Process-AHP) methods have been used to describe this pattern. There are different techniques to value an activity by itself and the target of every activity. In this study two decision matrices have been applied: "influence of factors on targets "and "pair wise comparison" matrices. In "influence of factors on targets" technique, every qualitative variable is weighted regarding to its importance degree, from the viewpoint of researchers, experts or policy-makers. Also, in this technique, the range of ranking, whether narrow or wide, depends on their points of view. Generally, every qualitative variable can meet one or more goals. In "pair wise comparison" matrix, at the first step, different goals are compared with each other. In the second step, every goal and qualitative variable are ranked and weighted. Finally, NG pricing pattern is made by using a mathematical explanatory behavior pattern. Since, Iran is a very extensive country with different geographic, cultural and economic characteristics, provinces are categorized regarding to their socio/economic development trend and geographic/climate conditions in NG pricing pattern framework. In this study, the special characteristics and their criteria, influencing on NG pricing pattern in residential sector, are defined as follows: Population Characteristics: Criteria: Population density Climate Conditions: Criteria: average annually temperature Demand-side Economic Conditions: Criteria: income per capita and income distribution index (Gini coefficient) 3

4 Supply-side Economic Conditions: Criteria: average cost of NG supply Political Characteristics: Criteria: long-term geo-politic and geo-economics (e.g. vicinity of border) Since, there is a very wide range for these criteria, classifying the different provinces, based on every effective factor, should be inevitable. Bracketing of criteria and classification of provinces based on them are carried out in a long and complex process. In this study, at the first step, every criteria related to its effective factors are evaluated. In the second step they are explained based on specified brackets. In the third step, the provinces are classified based on every bracketed criterion. The result of bracketing the criteria and classification of provinces has shown in Tables 1 to 4. Table1: Ranking of different Provinces based on Population density Rank 1 2 Criteria Person/km 2 More than 100 Name of Provinces Tehran, Gilan and Mazandaran Ghom and Hamedan 3 Kermanshah, W&E. Azerbijan,Khozestan,Ghazvin, Golestan and Ardabil Lorestan, Kordestan,Ch.M. Bakhtiyari, Markazi, Zanjan and Esfahan Kohkiloyeh and boyer ahmad, Boushehr, Fars, Ilam and Khorasan Hormozgan,Kerman,Sistan and Yazd 7 Less than 11 Semnan 4

5 Table2: Ranking of different Provinces based on Climate Condition Rank Climate Condition 6 Frizzing None 5 4 Too Cold Cold 3 Moderate Ardabil Name of Provinces Markazi, W.&E. Azerbijan, Zanjan, Kordestan, Ch.M. Bakhtiyari, Hamedan and Ghazvin Esfahan, Kerman, Semnan, Yazd, Kohkiloyeh and boyer ahmad, Gilan, Mazandaran, Fars, Ghom, Golestan, Lorestan, Khorasan, Kermanshah, Ilam, Sistan and Tehran 2 Warm Hormozgan and Khozestan 1 Too Warm Boushehr Table3: Ranking of different Provinces based on GDP per capita and Income distribution Index Adjusted GDP Rank per capita by Gini Coef. Name of Provinces 1 83 Tehran Markazi 67 Hormozgan and Ghazvin Esfahan, Khozestan, Kerman, Semnan, Yazd and Boushehr E Azerbijan, Gilan, Mazandaran, Fars and Ghom Golestan, Lorestan, W. Azerbijan, Khorasan, Ardabil, Kordestan,Ch.M. Bakhtiyari, Kermanshah, Hamedan, Zanjan and Ilam Sistan and Kohkiloyeh and boyer ahmad 5

6 Table4: Ranking of different Provinces based on Gas Supply Cost Rank Normalized AC Name of Provinces Tehran, Ch.M. Bakhtiyari, Esfahan and Fars Markazi and Hamedan 3 Ghazvin, Kohkiloyeh and boyer ahmd, Ghom, Kordestan, Zanjan and Kermanshah Yazd, Lorestan, Ardabil, Gilan and Semnan and Kerman Boushehr, Sistan, Khorasan and E Azerbijan Hormozgan, W. Azerbijan and Mazandaran Golestan, Khozestan and Ilam Quantification of effective factors, regards to "pricing targets" in residential sector is another step. It worth to mention, there are no direct and similar relationships between targets, necessarily. There can be tradeoff between specified targets as well. The targets of NG pricing in residential sector has been specified as follows: To increase consumers tendency toward switching to gas network (increasing NG consumption); To meet the revenue of gas supplier (supplier financing); To reduce environmental pollution; To increase welfare; To secure energy supply throughout all provinces ; The effective factors influence one or more targets but it worth to mention that the role and their importance are not the same. This dissimilarity arises from the utility differentials which would be obtained by each target. In other words, all specified targets and utilities will not have the same weigh. Therefore, it is necessary to rank and weight them in order to make gas pricing model. "Decision Matrix" has been applied to weigh all targets and utilities. This technique is based on comparing each pair of targets (or effective factors). 6

7 Therefore, by grading Preferences and applying a pre-defined multiplier coefficient to these grades, global preferences for all set of targets are obtained. The results taken from "decision Matrix" form these following priorities, increasing welfare, energy supply security and consumers tendency toward switching to gas network, supplier financing and reducing environmental Pollution, respectively (tables 5 and 6). Table5: influence of Effective factors on Goals in NG Pricing Pattern Criteria Goals Consumers Tendency to Switching to Gas Network Supplier Financing Reducing nvironmental Pollution Increasing Welfare Supply Security Population Density Reducing Temperature Income per Capita Average Cost Table6: Matrix of Paired Comparisons of Targets in NG PricingPattern Goals Consumers Tendency Switching to Gas Network Consumers Tendency to Switching to Gas Network Supplier Financing Reducing Environmental Pollution Increasing welfare Energy Supply Security Horizontal Variables preferences Supplier Financing Reduce Environmental Pollution Increase Welfare + 1 Energy Supply Security 0 Vertical Variables Preferences Horizontal Variables Preferences Sum of Preferences Grades based on Preferences Adjusted Grades

8 Based on "Decision Matrix" every effective factor- criterion (EFC) has following grades: Population density: 220 Reducing Temperature: 200 Income per Capita: 260 Average Cost: 280 The role of "average cost" and "income per capita" in NG pricing pattern are considerable. As mentioned before, the special characteristics of every province affect each of EFC s, crucially and directly and each criterion classified to some categories. The grade of each category, which shows its weigh and importance, has to be evaluated based on total grade of related criterion. To control the effectiveness of each category a non-linear mathematical function is defined as follows respect to number and importance of defined grades: G i = AD j 0 <= j <= 1 G: Grade of every effective factors-criterion has defined grope; D: Constant coefficient 10; i: Number of effective factors-criterion has defined grope; j: Importance coefficient of each effective factors- criterion; There are a direct relationship between j-th the coefficient and importance of effective factors. Therefore this coefficient would be changed based on different effective factors-criterion and policy decisions at present and in the future. It worth to mention, the different behavior patterns can explain the relationship between effective factors-criterion and this coefficient. The selection of each of these patterns depends on the role of related factors, determining NG pricing pattern and view of decision makers. Table 7 presents the result of different behavior patterns and classification of provinces by prices coefficients. 8

9 Table7: Classifying of Different Provinces based on Economic, Geographic and Population Conditions Province Economic of Gas Supply Economic of Provinces (Income per Capita) Climate Condition Population Condition Weighted Average of Points E. Azerbijan W. Azerbijan Ardabil Esfahan Ilam Boushehr Tehran Ch.M. Bakhtiyari Khorasan Khozestan Zanjan Semnan Sistan Fars Ghazvin Ghom Kordestan Kerman Kermanshah Kohkiloyeh and boyer ahmad Golestan Gilan Lorestan Mazandaran Markazi Hormozgan Hamedan Yazd In addition to pricing coefficient which is defined based on socio/economic characteristics, geographic/climate conditions, and utility coefficient as well, which is based on NG behavior consumption in different provinces, a price range adjustment coefficient is considered which determines upper and lower limits of gas provincial prices. This coefficient also adjusts the NG price in provinces which have special geographic/political conditions. 9

10 Regarding to this defined pricing mechanism, weighted average of selling prices have to meet the base price, which has been determined by framework of energy pricing policy (e.g. equal to or less/more than Average Incremental Cost (AIC) due to policy and political factors). In this paper, NG average selling price in 2001 (50 Rls/cm) has been considered as a base price. Also, the different scenarios have been stated, regarding to effective factors influences on provincial NG pricing pattern and provincial new ranking. There have some important points should be considered to choose the best NG provincial pricing patter, which are as follows: Ranking and evaluation of the important NG pricing indexes (income, cost indexes and geographic conditions); Variation (range) of price; The range of consumer surplus and the utility of gas consumption in different provinces; The selection of the best scenario can be carried out, base on two approaches: I. To create attractive and appropriate incentives for households to emigrate from big and crowded cities to small and noncrowded cities; II. To create relative balance among provincial NG prices, can meet to the most NG consumer satisfaction. In this case, variation (range) of prices should not make disturbances in gas pricing mechanism; The comparison of different scenarios, based on "price ranges" and "price variance" shows that limitation of prices range equal 25% (seventh scenario) is considered as the first priority. According to this scenario provincial price range and price variance are at the minimum levels, compared with other scenarios. Therefore, this scenario is selected as a 10

11 best scenario in this model. The maximum NG price belongs to Ilam, Hormozgan, Khozestan, Boushehr, Golestan, Semnan and Tehran provinces amounting to 61.4 Rls/cm and The minimum NG price belongs to province Kordestan amounting to 35 Rls/cm. In this scenario the maximum and minimum differentials and variance of NG prices are about 32 Rls/cm and respectively (Tables 8 and 9) Table8: Prioritizing of NG Pricing Different Scenarios based on Gas Selling Price in 2001 Subject Max. Price Min. Price Variance Diff. Max.& Min. Priority based on Price range Priority based on Variance

12 Table9: Comparative survey of Gas Provincial Prices in Different Scenarios based on Gas Selling Price in 2001 Province Ilam Hormozgan Khozestan Boushehr Golestan Semnan Tehran Sistan Yazd Kerman W. Azerbijan Khorasan Mazandaran Markazi E. Azerbijan Lorestan Kohkiloyeh and boyer ahmad Ghazvin Fars Gilan Ardabil Kordestan Zanjan Esfahan Kermanshah Ghom Ch.M. Bakhtiyari Hamedan

13 Policy implications Provincial NG prices basket in residential sector can be determined based on suggested pricing mechanism and regarding to different assumptions and scenarios. Some different points should be considered to choose the best scenario which is as follows: To maximize society welfare, in other word to meet socio-economic justice; To create incentives to energy and specially gas conservation by increasing price elasticity; To create no psychological sensitiveness Because of wide provincial NG prices; Encourage consumers to reveal their real tendency toward the gas consumption; To create legal and financial incentives for gas suppliers to expand the gas system; And finally, To create public satisfaction throughout provinces; By selecting the best price scenario, policy makers in energy sector would be able to create an appropriate climate to accept coming changes by consumers, to equate average gas selling price to average incremental supply cost in long term. It should be remembered that to achieve a suitable pricing pattern, it always necessary to create bilateral-cooperation between NG consumers and suppliers. Effort of NG residential consumers to coordinate their "willingness to pay" with "applied discriminatory prices" which is supported by policy makers and NG suppliers. Effort of NG residential suppliers (in all stages of supply: production, refining, transmission and distribution) to optimize the supply pattern and cost basket through supply costs reduction. 13

14 These bilateral efforts prepare an appropriate ground to apply a suitable provincial NG pricing pattern in an economic firm (to close together consumers' willingness to pay and supplier willingness to receive, e.g. Suppliers Average Incremental Cost). This matter in the long-run maximizes the socioeconomic benefits of society. References: M. Kinnan, sam martin, But we already do it, and other misunderstanding, SAVE International conference proceedings, G. Jergeas, V. cooke,f. Hartman, value engineering incentive clauses, American association of cost engineering, March Matthew Lewis, Planning to win, Hartpord newsroom. REUTERS, Dec Ken L. Smith, Applying value analysis to a value engineering program, AAHSTO VE conference, J. Samuel Martin, what s the difference?, SAVE International conference proceeding, 1997 J. George, C. Vernon, Value engineering during the project excution se, Transactions of AACE international,