DATA ENVELOPMENT ANALYSIS APPLIED TO EVALUATE THE USAGE OF OIL AND NATURAL GAS: SOUTH AMERICA CASE

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1 DATA ENVELOPMENT ANALYSIS APPLIED TO EVALUATE THE USAGE OF OIL AND NATURAL GAS: SOUTH AMERICA CASE Lucas Santos Menezes Oliveira Production Engineering Department - Fluminense Federal University Rua Passo da Pátria, 156, São Domingos, , Niterói, RJ, Brazil. lucassmo@yahoo.com Teresa Cristina Vilardo Domingues Correia Production Engineering Department - Fluminense Federal University Rua Passo da Pátria, 156, São Domingos, , Niterói, RJ, Brazil. cristinavilardo@yahoo.com.br João Carlos Correia Baptista Soares de Mello Production Engineering Department - Fluminense Federal University Rua Passo da Pátria, 156, São Domingos, , Niterói, RJ, Brazil. jcsmello@yahoo.com.br ABSTRACT This article has the object of evaluating the use of oil and natural gas by some South American countries. Since the beginning of the 20 th Century, these hydrocarbons have sponsored the industrialization of countries throughout the world. Since then, the consumption of these natural resources grew rapidly. The last ten years have been characterized by high oil prices. This situation made other countries vulnerable to Oil market variations. In this study, we analyzed South American countries performance based on 2005 data. The considered variables were production, consumption and proved reserves of oil and natural gas. We used Data Envelopment Analysis (DEA) to analyze the efficiency regarding usage and dependency of these resources. KEYWORDS: Data Envelopment Analysis, Inverted frontier method, Oil&Gas. [487]

2 1 Introduction Since the beginning of the 20th century, energy generated from hydrocarbons has been essential for economical and social development of countries throughout the world. Since then, its consumption of these natural resources grew rapidly. Nowadays these fuels stand for 60% of World Energy Matrix and 64% for South America. World Energy Matrix 6% 6% South America Energy Matrix 1% 37% 30% 41% 28% Oil Natural Gas Coal Hydroelectricity Nuclear Energy 5% Oil Natural Gas Coal Hydroelectricity Nuclear Energy 23% Fig. 1: World Energy Matrix 23% Fig. 2: South America Energy Matrix Oil consumption have sponsored countries industrialization and improved society s quality of life. But it has been also the reason for innumerous crisis, wars and instabilities among countries that rely on this source of energy. Events like OPEC (Organization of Petroleum Exporting Countries) oil crisis of 1973 and 1979 made oil prices rise from US$ 2.00 to US$ Gulf war was also another event that made oil prices rise up to unbearable prices due to uncertainties related to oil supply. The last ten years have been characterized by high oil prices. Its main causes are political and economical uncertainties of OPEC. These uncertainties made oil importing countries vulnerable to variations in oil supply and demand. Greater investments in oil exploration and production have been established in order to reduce foreign dependency on these fuels. Future perspectives point that oil will become scarce and its prices will reach historical marks. This will force countries to invest in alternatives sources of energy in order to reduce their dependency. Nevertheless specialists affirm that oil will still be the main source of energy around the world. Natural gas demand has grown 2% to 3% per year and until 2030 it will become the second most important source of energy. SOUTH AMERICA South America s oil usage scenario differs from the rest of the world s trend. Natural gas and hydroelectricity play more important parts in the continent energy matrix. Argentina and Brazil, respectively, are the strongest supporters. As they are considered clean sources of energy, CO2 emissions growth rate have been reduced. Nevertheless South American countries are still highly dependant on fossil fuels (figure 3), especially Argentina due to a widespread natural gas line network throughout its territory. [488]

3 Oil and Natural Gas in South America Energy Matrix 100% 80% 85% 81% 74% 60% 61% 59% 52% 40% 20% 0% Argentina Ecuador Venezuela Peru Colombia Brazil Fig. 3: Oil and Natural Gas in South America Energy Matrix Ecuador has one of the smallest natural gas proved reserves in South America. It exports 60% of the oil produced in the country. Due to the country lack of refining capacity, it still imports some fuels such as gasoline and diesel oil and liquefied petroleum gas, to satisfy domestic demand (Peláez-Samaniego, 2007). The country produces four times more hydrocarbons than what is consumed. It once was an OPEC member until Colombia currently faces internal challenges on its energy sector. The main causes are privatizations and market deregulations. Instabilities on oil exportation, low reliability on the supply of natural gas and electricity have developed together a scenario of economical uncertainties (Smith, 2005). Nevertheless, the country has innumerous reserves of coal and abundant renewable sources of energy. Due to low oil consumption, Colombia is considered selfsufficient (Kuntsi-Reunanen, 2007). Argentina is the country which consumes most natural gas in South America due to its extensive gas line. Because of its self-sufficiency in oil, impacts on internal economics caused by international uncertainties are less intense. Nevertheless in a long term analysis, natural gas consumption shall stop to grow or even contract, due to its low proven reserves of this fuel (Dubrovsky, 1998). The energy sector in Venezuela is dominated by oil exploration and production. It is the only country in South America to be an OPEC member. Its oil production is focused on Orinoco oil belt and this activity represents 80% of the country s exportations. Nevertheless large natural gas reserves and a high consumption of hydroelectric energy are major activities in the energy matrix (Kuntsi-Reunanen, 2007). Brazil is the country that consumes more fossil fuels in South America. It imports natural gas from Bolivia and recently achieved oil self-sufficiency status. Nevertheless it also imports light derivatives such as diesel oil and butane gas to meet national demand. It has the third largest hydroelectrically potential in the world and one of the greatest ethanol exporter. Such diversification results in a low oil and natural gas dependency rate in its energy matrix. Peru has extensive untapped energy potential in both natural gas and oil. But underinvestment by the state and lack of exploration of private companies made its oil production fall from 191,000 barrels/day in 1980 to 96,865 barrels/day in Peru is no longer self sufficient in oil but still exports heavy crude. Nevertheless since 1987 the country has imported rising quantities of light [489]

4 crude. Its natural gas reserves, especially Camisea field, represent almost seven times the size of existing proven oil reserves. Government estimates that the country will export natural gas to the West coast of the USA. This article stands between facing these country s features and analyzing future perspectives for oil and natural gas markets in South America. The primary objective is to evaluate the usage of oil and natural gas by South American countries. Data envelopment analysis was used to associate the usage of these fossil fuels to levels of industrial development and vulnerability of countries. The evaluation ranges the following countries: Venezuela, Brazil, Ecuador, Argentina, Colombia and Peru. Since this is a preliminary study, other countries were not included on this study. British Petroleum statistical review of World Energy June 2006 is the data source. The paper is organized as follows: In Section 2, DEA technique and its applications are briefly presented; Section 3 represents the methodology used, models and its results; In Section 4, results are evaluated and conclusions are drawn. 2 A Review of Data Envelopment Analysis - DEA Data Envelopment Analysis (DEA) is a mathematical tool that measure relative efficiency of decision making units (DMU) that use similar inputs to produce similar outputs. The efficiency calculation result is the weighted sum of outputs divided by the weighted sum of inputs. This definition requires a set of factor weights which are the decision variables. Each DMU is assigned the highest possible efficiency score by choosing the optimal weights for outputs and inputs (Yang, 2002). Besides, DEA models establish efficiency goals known as benchmarks for DMUs that are not efficient. A classic DEA model that has been used is the CCR model (Charnes et al. 1978), which considers constant returns to scale, i.e. assumes proportionality between inputs and outputs in the efficient DMUs (Soares de Mello et al. 2002). There is also BCC model (Banker et al. 1984) which considers variable returns to scale. It substitutes the input-output proportionality axiom for a convexity axiom. It allows DMUs that have low inputs come up with increasing returns to scale whereas DMUs that have high inputs come up with decreasing returns to scale. (Soares de Mello et al., 2005). In order to calculate the efficiency, there are two possible orientations. When it is input oriented, the problem is focused on reducing inputs in order to have a 100% efficient DMU. When it is output oriented, the focus is on increasing outputs to have an efficient DMU. Even tough DEA models allow the decision maker to establish an ordering of DMUs without issuing value judgments, they are benevolent to DMU. It occurs by considering only some of the evaluated variables in order to maximize the efficiency. This feature in DEA models results in many DMUs classified as efficient, which cause a low sorting of DMUs. The inverted frontier method (Yamada et al., 1994) is a pessimist evaluation of DMUs which allows us to sort those who are efficient. For that input and output variables are swapped from the original model. [490]

5 DEA methodology applied to oil&gas industries was successfully studied by Azadeh (2007), Vasconcellos (2006), Ebrahimipour (2006), Kashani (2005) and Ebrahimipour (2005). In this study, DEA models were applied in order to analyze the performance of South American countries regarding the usage of oil and natural gas and their dependency on these fuels. The variables used were volume of proven reserves, levels of annual production and consumption of these hydrocarbons. Each country with these features is considered as a DMU. Therefore, using different arrangements of input and output variables, there were generated industrial development and vulnerability indexes. An aggregated index was generated from the previous ones. The inverted frontier method was used to sort efficient DMUs. British Petroleum statistical review of world energy of 2006 was used as data source. 3 Application s results and discussions The values for variables proven reserves, annual production and annual consumption for each country in this study are represented in the table below. DMU COUNTRY PROVED RESERVES ANNUAL PRODUCTION ANNUAL CONSUMPTION BILLIONS OF BARRELS MILLIONS OF BARRELS MILLIONS OF BARRELS DMU_1 Venezuela 106, ,26 377,50 DMU_2 Brazil 13,75 695,62 746,19 DMU_3 Ecuador 5,77 203,31 49,84 DMU_4 Argentina 5,45 566,61 414,88 DMU_5 Colombia 2,19 243,36 120,95 DMU_6 Peru 3,18 41,64 57,17 Table 1: Proved Reserves, Production and Consumption of Oil and Natural Gas at 2005 Source: For the calculation of DMU s efficiency, ISYDS software - Integrated System for Decision Support (Ângulo Meza et al., 2005) was used. This software lists classical DEA models to be chosen and both possibilities of orientation. It also allows the use of the inverted frontier method and weight restrictions but this last method will not be used in our study. There were generated three evaluation models. Each model is detailed below. 3.1 Model 1 Industrial Development Index Since oil and natural gas play important roles in South America s energy matrix, in this first analysis we intend to measure the impact of these fuels in countries economy. These fuels are used in transportation, thermal energy generation, petrochemical industries, and agricultural products. The usage of oil and natural gas by South American countries was evaluated in order to compare its use with country s level of industrial development. The variable volume of proven reserves was used as an input; whereas annual production and consumption were set as outputs. The DEA model used was BCC with output orientation. By this, countries with low proven reserves, high fuel demands and production are considered efficient (Table 2). [491]

6 DMU Country Efficiency DMU_4 Argentina 1, DMU_2 Brazil 0, DMU_5 Colombia 0, DMU_1 Venezuela 0, DMU_3 Ecuador 0, DMU_6 Peru 0, Table 2: Industrial Development Index Argentina was the only country considered as efficient. This result partially corroborates with economical indexes such as GIP (Table 3). DMU Country GIP (millions of US$) DMU_2 Brasil ,00 DMU_4 Argentina ,00 DMU_1 Venezuela ,00 DMU_5 Colômbia ,00 DMU_6 Peru ,00 DMU_3 Equador ,00 Table 3: GIP at the end of 2005 Source: Model 2 Vulnerability Index South American countries are highly dependant to oil and natural gas, highlighted on figure 3. Because of this, we evaluated how vulnerable these countries are to political and economical instabilities, which may cause variations on fuel supply. Events such as government changes, nationalization of reserves or an unscheduled stoppage of production units may result in energy crisis for one or more countries. Thus, a vulnerability index was generated. For this analysis, proven reserves and annual production were considered as inputs whereas annual consumption was considered as an output. CCR was the model used and it was input oriented. By this, the annual production that was considered an input variable in the first model, now it is considered as an output. The main objective is to evaluate how vulnerable South American countries are when facing a sudden decrease in their production. Since fuel consumption has been increasing during the last 10 years, countries tend to be more dependants to these fuels. By this, countries with low production and less proven reserves are considered efficient and therefore vulnerable. Most of these vulnerable countries should rely on fuel importation to meet internal demands. [492]

7 DMU Country Efficiency DMU_6 Peru 1, DMU_2 Brazil 0, DMU_4 Argentina 0, DMU_5 Colombia 0, DMU_3 Ecuador 0, DMU_1 Venezuela 0, Table 4: Vulnerability Index Venezuela showed the lowest vulnerability index because it is an OPEC member. Since Ecuador used to be an OPEC member, it also has a low index. Peru and Brazil have not achieved selfsufficiency on these fuels and depends on importations to meet internal demands. 3.3 Model 3 Aggregated Index The aggregated index intends to unite in one sole analysis the results of the two previous models. The geometric mean average is used to generate the index. By this, we intend to measure the capability of countries to face oil and natural gas supplying variations with minimum damages to its industrial capacity. By this, efficient DMUs are those with a low vulnerability index and a high industrial development index. DMU Country Efficiency DMU_1 Venezuela 0, DMU_5 Colombia 0, DMU_3 Ecuador 0, DMU_4 Argentina 0, DMU_2 Brazil 0, DMU_6 Peru 0, Table 5: Aggregated Index The aggregated index results points Venezuela in first place due to a low vulnerability index. This country also is an OPEC member and its economy relies on fossil fuels. The results above partially corroborates with a Production-Consumption ratio (Table 6), known as Self-sufficiency ratio. Countries with an index greater than 1.0 are considered self sufficient. Ecuador presented the highest ratio but in the aggregated index it stood behind Venezuela and Colombia due to its low Industrial development index. DMU COUNTRY PRODUCTION / CONSUMPTION DMU_3 Ecuador 4,08 DMU_1 Venezuela 3,51 DMU_5 Colombia 2,01 DMU_4 Argentina 1,37 DMU_2 Brazil 0,93 DMU_6 Peru 0,73 Table 6: Production / Consumption Relation [493]

8 4 Conclusions Even though oil and natural gas are two of the main sources of energy, there s a great concern about the availability of these resources. This paper allowed us to evaluate South American countries efficiency regarding industrial development and vulnerability indexes. Data envelopment analysis was successfully used to generate these indexes through ratio of variables in different arrangements. The inverted frontier method showed itself as an additional tool to sort DMUs. Countries are becoming worried about these fuels s supply and investments on alternate sources of energy are being targeted. Renewable sources such as ethanol and biodiesel will decrease countries vulnerability caused by shortage of supply of fossil fuels. This study can be extended to evaluate other countries around the world. By doing this, internal politics can be developed in order to diversify their energy matrix and reduce their dependency to oil and natural gas. This evaluation can also be reproduced in annual terms in order to study countries performance regarding industrial development and vulnerability indexes. 5 References ANGULO MEZA, L., BIONDI NETO, L., SOARES DE MELLO, J.C.C.B., GOMES, E.G. (2005) ISYDS - Integrated System for Decision Support (SIAD - Sistema Integrado de Apoio a Decisão): a software package for data envelopment analysis model. Pesquisa Operacional, 25 (3), BANKER, R.D., CHARNES, A., COOPER, W.W. (1984) Some models for estimating technical scale inefficiencies in data envelopment analysis. Management Science, 30 (9), BILEN K., et al. (2007) Energy production, consumption, and environmental pollution for sustainable development: A case study in Turkey. Renew Sustain Energy Rev., doi: / j.rser CHARNES, A., COOPER, W.W., RHODES, E. (1978) Measuring the efficiency of decisionmaking units. European Journal of Operational Research, 2, DUBROVSKY, H. AND DAWIDOWSKI, L. (1998) Evolution of the energy sector in Argentina: an approach to the emissions inventory. Fuel and Energy abstracts, March 1998, 141. EBRAHIMIPOUR, V., SUZUKI, K. (2006) A synergetic approach for assessing and improving equipment performance in offshore industry based on dependability. Reliability Engineering and System Safety, 91 (1), pp EBRAHIMIPOUR, V., SUZUKI, K. (2005) An integrated model-based approach for evaluating and improving safety instruments in continuous process from the viewpoints of maintainability, reliability and availability - Case study: Offshore industry. Journal of Chemical Engineering of Japan, 38 (6), pp GREENE, D.L., HOPSON, J.L., LI, J. (2006) Have we run out of oil yet? Oil peaking analysis from an optimist s perspective. Energy Policy 34, [494]

9 KASHANI, H.A. (2005) State intervention causing inefficiency: An empirical analysis of the Norwegian Continental Shelf. Energy Policy, 33 (15), pp KUNTSI-REUNANEN, E. (2007) A comparison of Latin American energy-related CO2 emissions from 1970 to Energy Policy 35, PELÁEZ-SAMANIEGO, M.R., GARCIA-PEREZ, M., CORTEZ, L.A.B., OSCULLO, J., OLMEDO, G. (2007) Energy sector in Ecuador: Current status. Energy Policy Peru: Energy provision. The Economist Intelligence Unit Ltd. June 2007 Economy, available in accessed in February of 2007 SAIDEL, M.A., ALVES, S.S. (2003) Energy efficiency policies in the OECD countries. Applied Energy 76, SMITH, R. A., VESGA, D.R.A., CADENA, A.I., BOMAN, U., LARSEN, E., DYNER, I. (2004) Energy scenarios for Colombia: Process and content. Futures SOARES DE MELLO, J.C.C.B., ANGULO MEZA, L., GOMES, E.G., BIONDI NETO, L. (2005) Curso de Análise de Envoltória de Dados. Anais do XXXVII SBPO. SOARES DE MELLO, J.C.C.B., ANGULO MEZA, L., GOMES, E.G., SERAPIÃO, B.P., ESTELLITA LINS, M.P. (2003) Análise de Envoltória de dados no estudo da eficiência e dos benchmarks para companhias aéreas brasileiras. Pesquisa Operacional, v. 23, n.2, p STATISTICAL REVIEW OF WORLD ENERGY - JUNE 2006, BRITISH PETROLEUM, available in cations/statistical_energy_review_2006/staging/local_assets/downloads/pdf/statistical_re view_of_world_energy_full_report_2006.pdf, accessed in January of 2007 VASCONCELLOS, V.A., CANEN, A.G., LINS, M.P.E. (2006) Identificando as melhores práticas operacionais através da associação benchmarking-dea: O caso das refinarias de petróleo. Pesquisa Operacional, 26 (1), pp YANG, K., KUO, C. (2002) A hierarchical ahp/dea methodology for the facilities layout design problem. European Journal of Operational Research 147, [495]

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