Natural Gas Energy Consumption Research Articles

Research on the Coordination of Environmental Pollution and Industrial Structure Upgrading from the Coupling Perspective

Economic transformation and industrial structure upgrading are critical to achieving environmentally sustainable development. Based on the inaccurate data analysis of the VEC model and the logarithmic average exponential decomposition method, this paper proposes an analysis of the two-way coupling relationship between natural gas energy consumption and environmental pollution. Through the identification of conceptual models of the re-linking and decoupling relationship between energy consumption and environmental pollution, two-dimensional endogenous variables and exogenous variables are determined. The research results show that both energy consumption and CO2 emissions are on the actual data statistical line, with an error of 0. At the end, the article puts forward policy recommendations to promote the coordinated development of urban agglomeration environmental pollution and industrial structure upgrading.

Analyzing the effect of natural gas, nuclear energy and renewable energy on GDP and carbon emissions: A multi-variate panel data analysis

Internationally, the importance of clean energy is greatly appreciated in the context of sustainable development and to protect the atmosphere. Therefore, the objective of this article is to determine the effect of natural gas, renewable energy and nuclear energy consumption on economic growth and carbon dioxide emissions in the ten highest CO2 emitting countries within a multivariate context for the duration of 1990–2014. The panel co-integration test, panel fully modified ordinary least squares and panel heterogonous Dumitrescu and Hurlin causality assessment are used to analyze the estimation of long-run elasticity along with the course of causality among the variables. The panel co-integration test confirms the existence of a long-run equilibrium correlation among the variables. The findings of the long run elasticity and causality test reveal that natural gas does not contribute to economic growth and CO2 reduction like nuclear energy and renewable energy. However, except for natural gas, the expansion and improvement of renewable energy and nuclear energy are vital to avoid global warming and climate change as well as to promote economic growth.

How to efficiently promote distributed energy resources in China: Using a nonparametric econometric method

Most of the existing related studies use linear methods to investigate distributed energy resources, ignoring the objectively existing nonlinear relationships. This article makes improvement and uses a nonparametric additive regression model to explore China’s distributed energy resources. Estimated results show that technological progress exerts an inverted “U-shaped” nonlinear effect on distributed energy resources, due to the phased changes in R&D expenditures. Energy consumption structure also has an inverted U-shaped impact, which main reason is the changes in natural gas and renewable energy consumption. On the contrary, international oil prices have a positive U-shaped impact on distributed energy resources, because international oil prices are gradually increasing over time. Foreign oil dependence has an N-shaped nonlinear impact, which is mainly due to the changes in oil imports at different stages. The relationship between urbanization and distributed energy resources also presents an N-shaped pattern. This is mainly due to the periodic changes in the growth rate of the number of residents using natural gas. The research results can provide empirical support for the government to formulate targeted distributed energy policies at different stages.

Destek Vektör Makineleri ve Türkiye’deki Enerji Santrallerinde Doğal Gaz Tüketimi Üzerine Bir Uygulama

Bu çalışmanın amacı, destek vektör makinelerinde regresyon yöntemini kullanarak Türkiye'deki enerji santrallerinin doğalgaz tüketimi üzerine ön kestirim yapmaktır. Bu amaçla, veri seti 2013-2018 yılları arasında Türkiye Enerji Piyasası Düzenleme Kurumu ve Enerji İşleri Genel Müdürlüğünden elde edilmiştir. Bu çalışmada ilk olarak, Türkiye'de doğalgazın enerji piyasasındaki yeri, birincil enerji kaynakları içindeki payı, üretim, tüketim, ithalat ve ihracat değerleri incelenmiştir. Bu değerlerin ölçümündeki farklılıklardan dolayı, ilgili veri seti istatistiksel analizden önce standartlaştırılmıştır. Enerji santralleri tüketimi bağımlı değişken iken; sanayi tüketimi, şehir tüketimi, üretim, ithalat ve ihracat değerleri bağımsız değişken olarak belirlenmiştir. Destek Vektör Regresyonda kullanılan tüm kernel fonksiyonları (Doğrusal, Polinomiyal, Radyal Tabanlı Fonksiyon (RTF) ve Sigmoid) test edilmiştir. En küçük Hata Kareler Ortalaması (HKO)'na sahip olan RTF tahmin kernel fonksiyonu olarak seçilmiştir. Daha sonra, destek vektörler, ağırlıklar ve karar sabiti belirlenmiştir. Ağırlıklar ve destek vektörler çarpılıp sapma eklenerek, son model elde edilmiştir. Son model yardımıyla da Mayıs-Aralık 2018 için Türkiye'deki enerji santrallerinin doğalgaz tüketimlerine ait önkestirimler yapılmıştır. Sonuçlar ilgili tablolarda gösterilmiştir.

Another outlook to sector-level energy consumption in Pakistan from dominant energy sources and correlation with economic growth.

The present study seeks to investigate the sector-level energy consumption of oil and natural gas and to explore the linkage between economic growth, households, agriculture, industry, power, fertilizers, and commercial sector in Pakistan for the period of 1980-2016. The energy sector of Pakistan is facing severe crisis from the last few years due to inadequate production and supply. Long-lasting deficits of natural gas and oil, the two supreme types of fuel in Pakistan, had detrimental consequences for the growth as well as for the economic development. An autoregressive distributed lag (ARDL) method and Granger causality test under vector error correction model (VECM) were employed to check the association among the variables. Furthermore, the innovative accounting method was used to investigate the responsiveness of each variable to another within the study framework. Empirical results show long-run association among the variables, as oil consumption in the agriculture and power sector show a positive effect on Pakistan's economic growth. Similarly, energy consumption from natural gas in the households and fertilizers as well as in the industry sector has had a constructive association with economic growth. In contrast, energy consumption from oil in the households and industry sectors has adverse association with economic growth, while natural gas consumption in the commercial sector has negative linkage with economic growth. Possible steps should be taken by the Government of Pakistan to enhance the production of oil and natural gas from other alternatives to meet the requirements of these sectors.

Efficiency improvement, consumption reduction and optimization of high-sulfur natural gas sweetening units

To increase purified gas production and reduce the comprehensive energy consumption of high-sulfur natural gas sweetening unit, a process simulation model was established by using ProMax based on the field operation data in the Sinopec Puguang Natural Gas Purification Plant. Then, sensitivity analysis and optimization study were carried out on the main operating parameters, including circulation rates, the concentrations and the inlet temperatures of primary and secondary absorption towers of MDEA (methyldiethanolamine) solutions. Furthermore, the effects of reduction of the feed gas load and pressure and increasement of H2S content on the quality and yield rates of purified gas were analyzed under the optimized operating conditions with the actual field situations. And the following research results were obtained. First, the absorption selectivity of MDEA solutions can be improved by decreasing the circulation rates, concentrations and inlet temperatures of MDEA solutions, which is favorable for the increase of the yield rates of purified gas. Specifically, the circulation rate of MDEA solution is the main factor influencing the comprehensive energy consumption of a high-sulfur natural gas sweetening unit. Second, when the flow rate, pressure and H2S content of feed gas fluctuate, the purification requirements can be satisfied under the optimized operating conditions. Third, energy conservation under low flow rates of feed gas can be achieved by reducing the flow rates of regenerated steam and adjusting the position of MDEA solutions entering the secondary absorption tower. Fourth, as H2S content is increased by 1%, it is necessary to increase the circulation rate of MDEA solution by about 20 × 103 kg/h. Fifth, after parameter optimization, the yield rate of purified gas is increased by 0.5% and the comprehensive energy consumption is reduced by 19.1% under the operating condition of full load.

Does Environmental Tax Affect Energy Efficiency? An Empirical Study of Energy Efficiency in OECD Countries Based on DEA and Logit Model

OECD countries are the largest energy consuming economies in the world, improving energy efficiency and reducing pollution emissions is one of the important goals of the environmental tax policies of OECD countries. Based on the total factor energy efficiency index, this paper establishes an epsilon based measure-data envelopment analysis (EBM-DEA) model to measure the energy efficiency levels of 32 OECD countries during 1995–2016 when undesired outputs are included and not included. The effect of environmental factors on energy efficiency evaluation is compared by efficiency analysis and projection value analysis. On this basis, a Panel Logit model was established to empirically examine the impact of energy taxes on energy efficiency in 32 OECD countries. This paper finds that undesired output has a large impact on the energy efficiency level of OECD countries. Measuring energy efficiency levels without considering undesired outputs tends to lead to overestimation of the energy efficiency level of environmentally friendly countries and underestimate the energy efficiency level of countries that value environmental protection. The collection of energy tax has an important impact on energy consumption efficiency. Without considering the unexpected output, the energy tax has a significant impact on improving the efficiency of coal energy consumption. When considering the unexpected output, the energy tax has a significant impact on improving the efficiency of oil energy consumption. Regardless of the expected output or not, the energy tax has a positive effect on improving the efficiency of natural gas energy consumption. The experimental results also show that the energy structure and energy price have a negative impact on energy efficiency, while the progress of environmental protection technology and industrial structure have a positive impact on energy efficiency. Energy taxes have a “double dividend”. This paper argues that when evaluating a country’s energy efficiency, it should consider the undesired output factors of environmental constraints; governments should pay attention to the role of energy taxes in improving energy efficiency, improve the energy tax system, optimize industrial structure upgrades, stabilize energy prices and support the development of environmental technologies and improve energy efficiency.

The impact of natural gas and renewable energy consumption on CO2 emissions and economic growth in two major emerging market economies.

For the last three decades, both China and India are considered as the largest emerging market economies in the world. Both of these economies play an essential role in the global economy in terms of economic output and CO2 emissions. Hence, these countries are expected to play an important role in setting up environmental and sustainable development policies. Therefore, our paper aims to examine the role of natural gas and renewable energy consumptions on CO2 emissions and economic growth during 1965-2016 within a multivariate framework. The autoregressive distributed lag bounds testing approach to cointegration and vector error correction model (VECM) is employed to explore the long-run and causal nexus among the natural gas consumption, renewable energy consumption, coal and petroleum consumption, CO2 emissions, and economic growth, respectively. The empirical results show existence of long-run equilibrium association among the variables. The Granger causality results indicate that the short-run bidirectional causality between renewable energy consumption and economic growth in India, while no causality is found between these two variables in China. However, natural gas consumption causes economic growth in China whereas no causality is confirmed in India in the short-run. The findings further suggest that there is long-run bidirectional causality among the considered variables in both countries. Our paper addresses several important policy implications.

Exploring the impact of office building users' modeling approaches on energy use across Canadian climates

Modeling assumptions about how buildings will be used and operated have a major role in understanding the gap between predicted and measured energy use of buildings. The present research systematically quantified the impact of occupant modeling approaches on building energy performance across six climate zones of Canada. The impact of occupant modeling approaches was studied by altering the way occupant-related domains are modeled based on two modeling approaches: (1) standard assumptions, and (2) advanced models. Moreover, the impact of building operation modeling assumptions was studied using a simple approach for two domains, air handling unit (AHU) schedule and thermostat adjustment. The simulation results showed that the annual energy use of the building model was affected by advanced occupant models, mostly in milder climate zones. The predicted natural gas energy consumption using advanced models deviated by a factor of two from standard assumptions in the mildest climate zone of Canada. The occupant modeling domain that showed the highest main effect on energy use was lighting. Advanced occupant modeling generally led to a higher deviation of the predicted peak electricity load from standard assumptions in colder climates. AHU schedules and advanced operators’ thermostat adjustment model had a higher impact on predicted energy use than occupant modeling.

Large scale residential energy efficiency prioritization enabled by machine learning

Cost-effective energy efficiency improvements in residential buildings can realize 30% energy reduction within this energy sector for the USA. Unfortunately, audits on residences to identify potential savings, generally based on detailed energy models, tend to over-estimate anticipated savings. This leads to wariness on the part of potential investors. We address this issue by taking an integrated data- and physics-based approach, using residential building geometrical and energy system characteristics (e.g., envelope, appliances, and HVAC systems), as well as historical energy consumption for each residence and weather data, to predict monthly natural gas energy consumption and savings from the adoption of individual energy saving measures. This approach requires only those geometrical and energy system characteristics associated with the greatest potential for realized savings which are also easy to obtain. We construct a dataset from a collection of houses with a wide range of energy effectiveness, and train a single statistical model that accurately predicts natural gas energy consumption in any of the individual residences. The model is then used to estimate the savings from most impactful energy efficiency investments for individual residences, as well as the collective grouping. The specific case considered here involves hundreds of university-owned student residences in the U.S. Midwest. The resulting machine-learning derived model is used to predict monthly natural gas consumption with a mean squared error of 0.00023 for unitary scaled cross-validation data. We use a nearest neighbor approach to validate savings estimates for virtually improved residences, identifying the surrogate residence most like an improved residence. This validation demonstrates a savings prediction accuracy to within 3.5% for most of the measures considered. The validated models are shown capable of prioritizing investments among the collective of residences considered. Sequential adoption of the most cost-effective energy efficiency measure among a group of residences renders a total energy reduction of 36%. The practical implications of this research are significant. This integrated machine-learning and physics-based approach to estimate savings could be used in any utility district and throughout all of the USA to enable district-wide residential energy reduction based upon sequential adoption of the most cost-effective energy measures.

A novel modification on preheating process of natural gas in pressure reduction stations to improve energy consumption, exergy destruction and CO2 emission: Preheating based on real demand

One of the tools for optimizing energy systems is the design of the system output based on real (desired) demand. Thermodynamic performance of natural gas pressure reduction stations are functions of inlet conditions. In order to investigate the impacts of changes in inlet pressure and temperature on performance of a natural gas pressure reduction station, energy consumption and exergy destruction of a natural gas pressure reduction station of 10,000 SCMH are evaluated for different inlet conditions. In order to improve the station performance, a novel modification is proposed in the present research based on the real demand of preheating, wherein thermodynamic operation of the regulator is modeled and minimum pre-heating temperature of natural gas is calculated based on desirable temperature at the regulator outlet (natural gas hydrate formation temperature). Indeed, once the temperature at the heater outlet reaches the calculated minimum temperature, the heater is turned off. Compared to conventional stations, the modified station exhibits at least 33% and 15% reductions in energy consumption and exergy destruction, respectively. The results of investigating the performance of two sample stations also show that by implementing the proposed modification, CO2 emission can be reduced by up to 80% or even higher.

Study on Spatial and Temporal Variability of Rural Household Energy Consumption in Plateau Mountain Areas of Southwest China

Rural energy consumption of China is only about 20% to total energy consumption, but the consumer number has accounted for 70% of the population. Rural household energy is closely related to life quality of farmers and regional ecological environment, which concerns building a new socialist countryside, and occupies an important position in the national energy consumption system. Nowadays, the quantity and quality of rural life energy has been greatly improved, but environment pollution caused by burning of residue straw and extensive use of non-renewable fossil fuels is spread. Guiding the rural life energy consumption to a efficient, clean, environment-friendly direction is urgent. Southwest Plateau Mountain is one of the traditional geographical divisions in China, with abundant biomass resources and distinctive regional characteristics. However, few studies has been conducted to research the change of the rural life energy consumption structure in the region. Therefore, the spatial and temporal variation of rural life energy consumption was analyzed through the rural energy data from 1992 to 2017. The results showed that total consumption and per capita consumption of energy in rural life presented a step change characteristic of increasing firstly and then decreasing from 1992 to 2017 (the highest was in 2008). Total consumption and per capita consumption of rural life energy in 2017 increased by 3.64% and 65.13% respectively compared to 1992. Coal, straw, firewood are the main types of rural energy consumption in this area, gas, oil, liquefied petroleum gas, natural gas, coal gas and solar energy consumption ratio have improved to varying degrees with the evolution of the year. The proportion of commercial energy consumption increased from 30.05% in 1992 to 57.60% in 2017, reflecting the energy structure transforming from the traditional patterns to the diversification, utility and commercialization utilization pattern. The regional distribution of energy consumption intensity basically showed the characteristics of north high and south low, energy consumption structure, demonstrating significant regional differences. The largest energy consumption energy in Sichuan was firewood, and it was coal in Yunnan, Chongqing and Guizhou, and electricity in Tibet. Taking the complementary energy consumption mode of commercial energy and non-energy commercial energy according to local conditions, promoting the efficient and clean use of straw, firewood and solar energy will be the inevitable choice for sustainable development of rural energy in mountain plateau areas in Southwest China.

THE IMPACT OF NATURAL GAS DEMAND ON RENEWABLE ENERGY DEVELOPMENT: A PANEL INVESTIGATION OF SIX ASIAN COUNTRIES

The aim of this study is to assess the causal relationship of natural gas demand on the development of renewable energy in East and Southeast Asia. Specifically, the study examine the growth of natural gas and renewable energy consumption in both regions , determine the correlation of both energies and then measure the significant magnitude of the macroeconomic indicators in order to determine the development of renewable energy. The focus on the study was one Asian countries including China, Japan, South Korea to represent East Asia and Malaysia, Indonesia and Thailand to represent Southeast Asia. These countries are selected since they have contribute to the largest consumption in natural gas among other Asian countries. Although each of these countries have large potential renewable energy resource, these research found there are similar potential resources among the countries in each region. The quantitative research approach was applied to suit the time series secondary data collection. Panel data model then applied to enable researcher to determine the significant of macroeconomic indicators towards the development of renewable energy in East and Southeast Asia. With total of 90 observations, the macroeconomic variables include: natural gas consumption, GDP per capita, economic growth, exchange rate and access to electricity from period 2000-2014. Based on regression analysis, the findings revealed there is a significant coefficient between natural gas consumption on renewable energy development. A high consumption of natural gas would drive up the share of renewable energy to total final energy consumed. It is because these energy sources are not competitors yet complementary. However, the coefficient magnitude in Southeast Asia is higher than in East Asia. Consequently, other macroeconomic indicators such as exchange rate, GDP and electricity consumption has different effect on each region. These indicators can help the policy makers to improve the renewable energy policy which currently being implemented in order to induce the pace of renewable energy development in Asia.

Water Heat Pump Application With Double Stage Back Injection

In this study, a dual-stage heat pump system has been designed to utilize the heat of the ground water and to benefit from the heat of this water. Groundwater, which has been heat-treated, is intended to be re-grounded by a second deep well. In this application, well water at a depth of 110 meters, will be used as energy source. Hot water production will be provided by using groundwater at 15 °C in the primary circuit of the heat pump. Groundwater, which is reduced to 10 °C by taking heat from the heat pump, will be passed over the heat exchanger and will be used as cooling energy source in the primary circuit of the cooling tower. This reduces the flow temperature of the cooling tower and the water flowing through the cooling tower; which will allow “free cooling”. In the first stage of the designed system, it was determined that natural gas energy consumption of 2,880,000 kWh / year could be achieved by using hot water at 55 ̊C, which is produced by water heat pump, in process heating.In the second stage of the system, the water cooled in the heat pump will be withdrawn from the cooling tower temperature operating in the range of 28-32 ̊C, to the range of 15-20 ̊C. The cooling units connected to the tower are expected to have a 40% reduction in energy consumption which will lead total of 90.000 kWh / year energy savings. As a result; 470,000 TL / year cost savings will be achieved; thanks to heat pump application and 9.100 m3/year water savings as well as 580,400 kg /year CO2 emission reduction.

Impact of shocks in domestic aggregate demand for natural gas on domestic aggregate energy consumption in China

This paper examines the possible asymmetric transmissions from domestic aggregate demand for natural gas to domestic aggregate energy consumption in China using time series data from 1970 to 2016. The nonlinear autoregressive distributed lags (NARDL) model is employed to check the possibility of long-term asymmetric nexus among variables. The empirical findings confirm the existence of symmetric cointegration between the domestic aggregate demand for natural gas and domestic aggregate energy consumption. The results also indicate that positive shocks in domestic aggregate energy consumption lead an increase in domestic aggregate energy consumption in both short-run and long-run. While the NARDL dynamic multiplier graph suggests that the positive component of domestic aggregate demand for natural gas has deep impact on domestic aggregate energy consumption.

Natural gas consumption and economic growth nexus for top 10 natural Gas–Consuming countries: A granger causality analysis in the frequency domain

In this study, the relationship between natural gas energy consumption and economic growth has been examined within the framework of the growth model for the top 10 natural gas–consuming countries from 1994 to 2015 using the recently developed panel Granger causality tests that allow for country-level heterogeneity. The results show that there is a long-run relationship between natural gas consumption and economic growth, and natural gas consumption has a significant and positive impact on economic growth in the long run. The Granger causality analysis in the frequency domain test results could be summarized as following. First, in terms of causality running from economic growth to natural gas consumption, there is a temporary relationship for Germany, and a permanent relationship for the United Kingdom and Thailand. Second, in terms of causality running from natural gas consumption to economic growth, there is a temporary relationship for Thailand. The main finding is that countries should increase the use of natural gas consumption to ensure energy security, reduce energy dependence, reduce carbon dioxide emissions, and encourage economic growth.

Forecasting Carbon Emissions Related to Energy Consumption in Beijing-Tianjin-Hebei Region Based on Grey Prediction Theory and Extreme Learning Machine Optimized by Support Vector Machine Algorithm

Carbon emissions and environmental protection issues have brought pressure from the international community during Chinese economic development. Recently, Chinese Government announced that carbon emissions per unit of GDP would fall by 60–65% compared with 2005 and non-fossil fuel energy would account for 20% of primary energy consumption by 2030. The Beijing-Tianjin-Hebei region is an important regional energy consumption center in China, and its energy structure is typically coal-based which is similar to the whole country. Therefore, forecasting energy consumption related carbon emissions is of great significance to emissions reduction and upgrading of energy supply in the Beijing-Tianjin-Hebei region. Thus, this study thoroughly analyzed the main energy sources of carbon emissions including coal, petrol, natural gas, and coal power in this region. Secondly, the kernel function of the support vector machine was applied to the extreme learning machine algorithm to optimize the connection weight matrix between the original hidden layer and the output layer. Thirdly, the grey prediction theory was used to predict major energy consumption in the region from 2017 to 2030. Then, the energy consumption and carbon emissions data for 2000–2016 were used as the training and test sets for the SVM-ELM (Support Vector Machine-Extreme Learning Machine) model. The result of SVM-ELM model was compared with the forecasting results of SVM (Support Vector Machine Algorithm) and ELM (Extreme Learning Machine) algorithm. The accuracy of SVM-ELM was shown to be higher. Finally, we used forecasting output of GM (Grey Prediction Theory) (1, 1) as the input of the SVM-ELM model to predict carbon emissions in the region from 2017 to 2030. The results showed that the proportion of energy consumption seriously affects the amount of carbon emissions. We found that the energy consumption of electricity and natural gas will reach 45% by 2030 and carbon emissions in the region can be controlled below 96.9 million tons. Therefore, accelerating the upgradation of industrial structure will be the key task for the government in controlling the amount of carbon emissions in the next step.

Actual validation of energy simulation and investigation of energy management strategies (Case Study: An office building in Semnan, Iran)

Due to the wide range of parameters effect in energy consumption within a building, it is not possible to make a proper decision about the impact of different energy efficiency strategies without simulation tools. In this study, the annual energy consumption of an office building in Semnan, Iran is simulated using DesignBuilder software. The validation of simulation results is down by monthly electricity and natural gas energy consumption billing records with the highest simulation error of about 1.6%. Three strategies are proposed to management the energy consumption, e.g. the replacement of single-glazed windows with low-emission double-glazed window, installation of a thermal insulation sheet on the external wall of the building and use of horizontal shadings on the outside and removal of the internal shades. The simulation results show that these strategies lead to 14%, 18% and 13% reduction in energy consumption, respectively. In order to revalidate the simulation, the strategy of replacing the existing windows with low-emission double-glazed is adopted and the actual energy consumption of the building is compared with the simulated value after one year, indicating a good agreement at this stage.

CO2 emissions, natural gas and renewables, economic growth: Assessing the evidence from China

This study aims to test the environmental Kuznets curve (EKC) for carbon dioxide (CO2) emissions in China by developing a new framework based on the suggestion of Narayan and Narayan (2010). The dynamic effect of natural gas and renewable energy consumption on CO2 emissions is also analyzed. Considering the structural break observed in the sample, a series of econometric techniques allowing for structural breaks is utilized for the period 1965–2016. The empirical results confirm the existence of the EKC for CO2 emissions in China. Furthermore, in both the long-run and the short-run, the beneficial effects of natural gas and renewables on CO2 emission reduction are observable. In addition, the mitigation effect of natural gas on CO2 emissions will be weakened over time, while renewables will become progressively more important. Finally, policy suggestions are highlighted not only for mitigating CO2 emissions, but also for promoting growth in the natural gas and renewable energy industries.

Evolutionary Design of a Carbon Dioxide Emission Prediction Model using Genetic Programming

Weather pollution is considered as one of the most important, dangerous problem that affects our life and the society security from the different sides. The global warming problem affecting the atmosphere is related to the carbon dioxide emission (CO2) from the different fossil fuels along with temperature. In this paper, this phenomenon is studied to find a solution for preventing and reducing the poison CO2 gas emerged from affecting the society and reducing the smoke pollution. The developed model consists of four input attributes: the global oil, natural gas, coal, and primary energy consumption and one output the CO2 gas. The stochastic search algorithm Genetic Programming (GP) was used as an effective and robust tool in building the forecasting model. The model data for both training and testing cases were taken from the years of 1982 to 2000 and 2003 to 2010, respectively. According to the results obtained from the different evaluation criteria, it is nearly obvious that the performance of the GP in carbon gas emission estimation was very good and efficient in solving and dealing with the climate pollution problems.