World transport energy demand modelling : Methodology and elasticities

This paper presents the International Energy Agency's (IEA) approach of modelling transport energy demand. Fuel demand, which is not a demand per se, is derived, whenever possible, from the economic activity in the transport sector and not estimated directly, ie using one equation or (simultaneous) equation system. In general, the transport models employ a ‘two-step-approach’: in the first step, transport activity, the sector's relevant energy service, is estimated econometrically. In the second step, the transport activity projections are then combined with estimates of efficiency improvements, car turnover rates and diesel/gasoline penetration assumptions in order to arrive at projections of fuel demand. The principal advantages of this approach are that the relevant energy services are modelled and that, for model simulation, efficiency improvements can be dealt with explicitly. The effectiveness of economic instruments is a function of the reaction of consumers (and businesses) to income and price changes. An in-depth understanding of income and price elasticities of transport demand and transport energy demand is important in order to be able to assess the effectiveness of policies considered. The paper also shows the underlying long-term income and price elasticities for OECD and non-OECD regions.     

Transport energy demand modeling of South Korea using artificial neural network

Artificial neural network models were developed to forecast South Korea's transport energy demand. Various independent variables, such as GDP, population, oil price, number of vehicle registrations, and passenger transport amount, were considered and several good models (Model 1 with GDP, population, and passenger transport amount; Model 2 with GDP, number of vehicle registrations, and passenger transport amount; and Model 3 with oil price, number of vehicle registrations, and passenger transport amount) were selected by comparing with multiple linear regression models. Although certain regression models obtained better R-squared values than neural network models, this does not guarantee the fact that the former is better than the latter because root mean squared errors of the former were much inferior to those of the latter. Also, certain regression model had structural weakness based on P-value. Instead, neural network models produced more robust results. Forecasted results using the neural network models show that South Korea will consume around 37 MTOE of transport energy in 2025.     

基于时间序列ARMA模型的广东省能源需求预测

广东省能源消耗量大,自给率低,能源供需矛盾已成为影响经济发展的重要因素,准确预测未来能源需求对于制定合理的经济发展战略和能源安全战略有着重要的借鉴意义。采用1979-2006年广东省能源消费总量数据,并根据建模要求对数据进行处理,在此基础上利用时间序列相关理论及ARMA模型对广东省未来能源需求量进行了相关预测,并得出能源需求的模型。从检验结果来看,此模型误差率低,预测效果好。     

Study on the hydrogen demand in China based on system dynamics model

Reasonable estimation of hydrogen energy and other renewable energy demand of China's medium and long-term energy is of great significance for China's medium and long-term energy plan. Therefore, based on both China's future economic development and relative economic theory and system dynamics theory, this article analyzes qualitatively the internal factors and external factors of hydrogen energy demand system, and makes the state high and low two assumptions about China's medium and long-term hydrogen demand according to the different speed of China's economic development. After the system dynamic model setting up export and operation, the output shows the data changes of the total hydrogen demand and the four kinds of hydrogen demand. According to the analysis of the output, two conclusions are concluded: The secondary industry, not the tertiary industry (mainly the transportation), should be firstly satisfied by the hydrogen R&D and support of Government policy. Change of Chinese hydrogen demand scale, on basis of its economic growth, can not be effective explained through Chinese economic growth rate, and other influencing factor and mechanism should be probed deeply.     

Modelling future private car energy demand in Ireland

Targeted measures influencing vehicle technology are increasingly a tool of energy policy makers within the EU as a means of meeting energy efficiency, renewable energy, climate change and energy security goals. This paper develops the modelling capacity for analysing and evaluating such legislation, with a focus on private car energy demand. We populate a baseline car stock and car activity model for Ireland to 2025 using historical car stock data. The model takes account of the lifetime survival profile of different car types, the trends in vehicle activity over the fleet and the fuel price and income elasticities of new car sales and total fleet activity. The impacts of many policy alternatives may only be simulated by such a bottom-up approach, which can aid policy development and evaluation. The level of detail achieved provides specific insights into the technological drivers of energy consumption, thus aiding planning for meeting climate targets. This paper focuses on the methodology and baseline scenario. Baseline results for Ireland forecast a decline in private car energy demand growth (0.2%, compared with 4% in the period 2000–2008), caused by the relative growth in fleet efficiency compared with activity.     

Road transport energy demand in West Africa: a test of the consumer-tolerable price hypothesis

This study estimates road transport energy demand (both aggregate energy fuel and gasoline energy fuel) in West Africa using the Pooled Mean Group Estimate and the Panel FMOLS. Primarily, we test for the nonlinearity in the price effects (hereafter referred to as the consumer-tolerable price hypothesis), which is motivated by Adom (2017. “The long-run Price Sensitivity Dynamics of Industrial and Residential Electricity Demand: The Impact of Deregulating Electricity Prices” Energy Economics 62: 43–60). First, for the baseline model, we find that, in the long-run, the energy conservation potency of pricing tools is restrained due to the presence of a rebound effect. Similar result is obtained in the short-run with evidence of cross-sectional differences. Second, in the long-run, we find that the demand–price relation is an inverted U-shaped, but we could only confirm this for Nigeria and Ghana; this suggests that, in the long-run, price disincentive tools have to be higher than a required price threshold in order to induce energy conservation behaviours in these economies.     

Reduction potentials of energy demand and GHG emissions in China's road transport sector

Rapid growth of road vehicles, private vehicles in particular, has resulted in continuing growth in China's oil demand and imports, which has been widely accepted as a major factor effecting future oil availability and prices, and a major contributor to China's GHG emission increase. This paper is intended to analyze the future trends of energy demand and GHG emissions in China's road transport sector and to assess the effectiveness of possible reduction measures. A detailed model has been developed to derive a reliable historical trend of energy demand and GHG emissions in China's road transport sector between 2000 and 2005 and to project future trends. Two scenarios have been designed to describe the future strategies relating to the development of China's road transport sector. The ‘Business as Usual’ scenario is used as a baseline reference scenario, in which the government is assumed to do nothing to influence the long-term trends of road transport energy demand. The ‘Best Case’ scenario is considered to be the most optimized case where a series of available reduction measures such as private vehicle control, fuel economy regulation, promoting diesel and gas vehicles, fuel tax and biofuel promotion, are assumed to be implemented. Energy demand and GHG emissions in China's road transport sector up to 2030 are estimated in these two scenarios. The total reduction potentials in the ‘Best Case’ scenario and the relative reduction potentials of each measure have been estimated.     

Prospect of the upper limit of the energy demand in China from regional aspects

The aim of this article is to indicate the upper limit of the annual energy demand in China, taking into consideration regional demand trends and projecting these trends into the distant future. The upper limit of energy consumption is not strictly the maximum amount of consumption. It means that the actual consumption will possibly exceed this level but not by much.     

Structural change and Thailand energy demand

This paper examines the impact of changes in the structure of the economy, radical changes in economic policy and oil price shocks on the relation between Thailand energy demand and its macroeconomic determinants. The impact of these structural changes on the relationship between energy consumption, income, energy prices and structural variation is examined through unit root and cointegration tests, the cointegration relationship and the error correction model. Methods which endogenize the location of an a priori unknown break point are employed to assess the impact of structural change. In general, the recognition of structural change has lead to some unique insights. In particular, the results of some of the conventional unit root and cointegration tests are reversed once structural changes are recognized. Estimates from the cointegrating regression imply long-run income, price, and structural variation elasticities of 0.568, −0.600 and 1.046, respectively. In comparison, estimates from the error correction model suggest a higher short-run income elasticity (0.788) but lower short-run price and structural variation elasticities (−0.522 and 0.491, respectively). One of the important implications of the estimates pertains to the low price elasticity for aggregate energy demand which implies that the over-pricing of energy as a policy instrument is not likely to be very influential for restraining future energy demand. Additionally, taxes on energy prices are unlikely to achieve government goals for energy conservation and environmental improvement, although they may well be efficient for raising revenue. Copyright © 2006 John Wiley & Sons, Ltd.     

Trends in passenger transport energy use in South Korea

Having a clear understanding of transport energy use trends is crucial to identifying opportunities and challenges for efficient energy use for the transport sector. To this date, however, no detailed analysis has been conducted with regard to rapidly growing passenger transport energy use in South Korea. Using bottom-up data developed from a variety of recent sources, we described the trends of transport activity, energy use, and CO₂ emissions from South Korea’s transport sector since 1986 with a particular focus on its passenger transport. By decomposing the trends in passenger transport energy use into activity, modal structure, and energy intensity, we showed that while travel activity has been the major driver of the increase in passenger transport energy use in South Korea, the increase was to some extent offset by the recent favorable structural shift toward bus travel and away from car travel. We also demonstrated that while bus travel has become less energy intensive since the Asian Financial Crisis, car travel has become increasingly energy intensive.     

Price and expenditure elasticities of residential energy demand during urbanization: An empirical analysis based on the household-level survey data in China

Urbanization, one of the most obvious characteristics of economic growth in China, has an apparent “lock-in effect” on residential energy consumption pattern. It is expected that residential sector would become a major force that drives China's energy consumption after urbanization process. We estimate price and expenditure elasticities of residential energy demand using data from China's Residential Energy Consumption Survey (CRECS) that covers households at different income levels and from different regional and social groups. Empirical results from the Almost Ideal Demand System model are in accordance with the basic expectations: the demands for electricity, natural gas and transport fuels are inelastic in the residential sector due to the unreasonable pricing mechanism. We further investigate the sensitivities of different income groups to prices of the three types of energy. Policy simulations indicate that rationalizing energy pricing mechanism is an important guarantee for energy sustainable development during urbanization. Finally, we put forward suggestions on energy pricing reform in the residential sector based on characteristics of China's undergoing urbanization process and the current energy consumption situations.     

Trends of energy demand in the Middle East: A sectoral level analysis

The Middle East region is a key player in the world energy market today. It holds approximately over 50% of the world's proven fossil fuel reserves. Yet, the region is significantly challenged by the large dependence on finite fossil fuel resources in its primary energy supply. The intricate relationship between climate change mitigation and the development of energy systems underlines great uncertainty over the future of energy development in the Middle East. Such uncertainty is greatly linked to growing energy demands and the region's capacity to transition to low‐carbon energy systems. Over the past 20 years, the total primary energy demand in the Middle has almost tripled due to rapid population growth and economic development. Notably, most of the growing energy demand was concentrated in 5 countries, Iran, Saudi Arabia, Iraq, Kuwait, and the UAE. These 5 countries represented around 82% of the total primary energy demand in 2015, with Saudi Arabia and Iran alone accounted for 60%. The core question of this paper is what are the possible implications of growing energy demands in these countries and which sectors will entail significant increases in the projected energy requirements? The significance of the work presented here stems from analyzing 4 major countries that constitute the largest share in Middle East's total energy consumption and associated emissions. Examining these 4 countries together is important to highlight how future increase in these countries could largely affect the overall energy demand from the Middle East region in the next 20 years. Thus, the scope of the paper is looking at energy demand implications in 4 countries, Iran, Saudi Arabia, Kuwait, and the United Arab Emirates (UAE). Iraq is excluded from the analysis due to the large political uncertainty associated with Iraq's energy development. Here, a regression model is used to forecast energy demand from 5 economic sectors across the 4 countries using projected increase in population and gross domestic product (GDP) by 2030. Results indicate that most of the projected energy demand will be from Iran and Saudi Arabia. In addition, industry and transportation sectors will witness the largest increase among the 5 sectors examined in the paper. For instance, industry and transportation sector will collectively account for 52% and 67% of the projected energy demand in Iran and Saudi Arabia, respectively. Such results are important to highlight when ascertaining sectoral level implications of future energy demands and to determine potential areas where energy savings can be made.     

ARIMA forecasting of primary energy demand by fuel in Turkey

Forecasting of energy demand in emerging markets is one of the most important policy tools used by the decision makers all over the world. In Turkey, most of the early studies used include various forms of econometric modeling. However, since the estimated economic and demographic parameters usually deviate from the realizations, time-series forecasting appears to give better results. In this study, we used the Autoregressive Integrated Moving Average (ARIMA) and seasonal ARIMA (SARIMA) methods to estimate the future primary energy demand of Turkey from 2005 to 2020. The ARIMA forecasting of the total primary energy demand appears to be more reliable than the summation of the individual forecasts. The results have shown that the average annual growth rates of individual energy sources and total primary energy will decrease in all cases except wood and animal–plant remains which will have negative growth rates. The decrease in the rate of energy demand may be interpreted that the energy intensity peak will be achieved in the coming decades. Another interpretation is that any decrease in energy demand will slow down the economic growth during the forecasted period. Rates of changes and reserves in the fossil fuels indicate that inter-fuel substitution should be made leading to a best mix of the country's energy system. Based on our findings we proposed some policy recommendations.     

Trends in passenger transport and freight energy use in Spain

This paper provides for the first time a complete analysis of recent trends in activity, carbon emissions, modal shares, energy intensities, vehicle use and fuels in the Spanish transport system from 1990 to 2008 and discusses policy options. Passenger and freight activities have increased in Spain and are projected to continue, presenting a challenge for sustainable mobility efforts; emissions have increased, mainly fueled by the rise in activity; modal shares have pulled away from public transport, with a decrease in bus and rail, towards an increase in car and air travel; energy intensities, though initially decreasing, are currently increasing; and fuel use has taken over 37% of Spanish final energy consumption.     

Reevaluation of Turkey's hydropower potential and electric energy demand

This paper deals with Turkey's hydropower potential and its long-term electric energy demand predictions. In the paper, at first, Turkey's energy sources are briefly reviewed. Then, hydropower potential is analyzed and it has been concluded that Turkey's annual economically feasible hydropower potential is about 188 TWh, nearly 47% greater than the previous estimation figures of 128 TWh. A review on previous prediction models for Turkey's long-term electric energy demand is presented. In order to predict the future demand, new increment ratio scenarios, which depend on both observed data and future predictions of population, energy consumption per capita and total energy consumption, are developed. The results of 11 prediction models are compared and analyzed. It is concluded that Turkey's annual electric energy demand predictions in 2010, 2015 and 2020 vary between 222 and 242 (average 233) TWh; 302 and 356 (average 334) TWh; and 440 and 514 (average 476) TWh, respectively. A discussion on the role of hydropower in meeting long-term demand is also included in the paper and it has been predicted that hydropower can meet 25–35% of Turkey's electric energy demand in 2020.     

Assessment of long-term sustainable end-use energy demand in Romania*

Romania is the 10th largest economy in EU-28 and also one of the fastest growing economies in the region. An end-use energy demand model is developed for Romania to assess energy requirement by sector and by end-use for 2015–2050 period. Industry would surpass residential sector as the largest final energy-consuming sector from 2035 onwards. Services sector would exhibit the fastest growth of energy consumption. Despite expected decline in country’s population, demand for electricity would grow in the future driven by increased household income and expanded services sector, which is relatively electricity intensive. Still, Romania’s per capita electricity consumption would be about half of the EU-28 average. At the end-use level, thermal processes in industry, space heating in the residential and services, and road passenger travel in transport sector would be dominant throughout the study period. Improvement of energy efficiency in the heating system exhibits the highest potential of energy saving.     

用组合预测模型对我国能源需求量的研究

随着中国经济的快速发展,能源消费不断提高。利用能源消费量的历吏数据,建立了我国能源消费系统的ARMA模型和灰色预测模型的组合模型。通过组合模型和ARMA模型、灰色预测模型的具体比较分析,证明组合模型更为易行、有效,可以作为我国及地区未来能源消费量预测的有效工具。     

Hybrid energy management with respect to a hydrogen energy system and demand response

A Hybrid Energy System (HES) is a mechanism that combines multiple sources of energy connected together to achieve synchronised energy output. However, increased energy consumption, operator energy expenses, and the potential environmental impact of increased emissions from the exhaustion of non-renewable energy resources (fossil fuel) pose major challenges to HES. This research is to conduct energy management strategy based on a demand response (DR) program and a hydrogen storage system by designing a Program Logic Controller (PLC) unit. The hybrid system is evaluated by comparing different scenarios such as a hydrogen energy system and demand response. The purpose of this research is to reducing peak demand, minimise the cost of the system and also to extract surplus power generation out of the rate of the battery. This can be achieved by improving the system performances and by eliminating any degradation at the early stages. Organisations or companies must be sure their systems are working properly and that their investments will pay off.     

Weather, the estimation of residential energy demand relationships and the reporting of data

The need for adjusting energy related data, especially weather data, prior to estimating demand relationships is pointed out. A previous study by Rushdi in Energy Economics and other studies are used as examples. The lack of such adjustments could affect greatly the estimated results. The desirability of making data available along with an article is also pointed out.     

Projection of future transport energy demand of Thailand

The objective of this study is to project transport energy consumption in Thailand for the next 20 years. The study develops log-linear regression models and feed-forward neural network models, using the as independent variables national gross domestic product, population and the numbers of registered vehicles. The models are based on 20-year historical data between years 1989 and 2008, and are used to project the trends in future transport energy consumption for years 2010–2030. The final log-linear models include only gross domestic product, since all independent variables are highly correlated. It was found that the projection results of this study were in the range of 54.84–59.05 million tonnes of oil equivalent, 2.5 times the 2008 consumption. The projected demand is only 61–65% of that predicted in a previous study, which used the LEAP model. This major discrepancy in transport energy demand projections suggests that projects related to this key indicator should take into account alternative projections, because these numbers greatly affect plans, policies and budget allocation for national energy management.