Abstract
Over the last decade there has been a gradual change in the global energy landscape, with fast-growing emerging markets overtaking the traditional centers in terms of energy demand. International Energy Outlook 2017 forecasts that energy consumption in non-OECD countries would increase by 41% between 2015 and 2040 in contrast to a 9% increase in OECD countries. The chapter focuses on two major areas: (1) examining the changing pattern of the composition of energy use in the two selected countries of Asia (India and China) and (2) examining the short-run and long-run relationship among energy use, GDP per capita, energy intensity, use of electricity power, extent of urbanization. Using ARDL bound test for the period 1990 to 2014 for the World Development Indicator data 2017-18, it reveals that the powerhouses of global energy demand growth are led by the developing economies of Asia (i.e., China, India, Indonesia, Morocco, Brazil, Singapore, and Thailand). In the case of India, a long-run association has been found between energy use and GDP per capita, energy intensity, use of electricity power, and extent of urbanization, but no instances are for China.
TopIntroduction
Of late, there has been a gradual change in the global energy landscape, with fast-growing emerging markets overtaking the traditional centres in terms of energy demand. The composition of energy mix is also undergoing a major change which is being guided by technological innovations or inventions and environmental concerns. One of the fundamental characteristics of the energy transition mapped out by this Energy Outlook Survey 2017 is gradual movement towards de-carbonization of the fuel mix. Significant improvements in the competitiveness of renewable energy have led to the increases in renewable, together with nuclear and hydro energy which expected to provide around half of the increase in global energy till 2035. Natural gas is expected to grow faster than oil or coal, helped by the rapid growth of liquefied natural gas(LNG) increasing the accessibility of gas across the globe. Compared to the growth of vehicles, oil demand continues to increase at a slower pace due to fuel efficiency and technological improvements, such as electric vehicles, battery driven cars etc has been increasingly implemented to keep the environment green. The expansion in the overall demand for energy is expected to continue as increasing prosperity in fast-growing emerging economies lifts billions of people from low incomes. But due to increasing attention in improvements in energy efficiency around the world the extent of this increase is likely to be curbed and making it more sustainable.
Global primary energy consumption grew strongly in 2017, led by natural gas and renewable, with coal’s share of the energy mix continuing to decline. Primary energy consumption growth averaged 2.2% in 2017, up from 1.2% in 2016.By fuel, natural gas accounted for the largest increment in energy consumption, followed by renewable and then oil. Energy consumption rose by 3.1% in China. China was the largest growth market for energy for the 17th consecutive year.
Carbon emissions from energy consumption increased by 1.6%, after little or no growth for the three years from 2014 to 2016.Despite the unusually strong growth in the OECD, the vast majority of the increase in global energy consumption came from the developing world, accounting for nearly 80% of the expansion.
Key Terms in this Chapter
Population Growth: Population growth refers to the increase in the number of individuals in a population in a particular year. Many of the world's countries, including many in Sub-Saharan Africa, the Middle East, South Asia and South East Asia, have seen a sharp rise in population in recent times. High population growth puts strain on natural resources, energy use and consumption pattern, food supplies, fuel supplies, employment, housing, etc.
Economic Growth: Economic growth basically refer to an increase in the amount of goods and services produced per head of the population over a period of time. It is conventionally measured as the percent rate of increase in real gross domestic product, or real GDP, after taking into account the inflation-adjusted market value of the goods and services produced by an economy over time.
Time-Series Analysis: A set of statistics, usually collected at regular intervals. Secondary time series data for two countries of Asia viz. India and China over the period 1990-2014 has been used to provide an empirical support to the various comparative analysis. Correlation and regression analysis are used to arrive at the results. This has been collected from two main sources: (1) BP Statistical Review of World Energy 2018 and (2) World Development indicator 2017-18.
Energy Consumption Pattern: Consumption pattern of energy includes consumption of coal, crude oil and natural gas, petroleum products, electricity. India will overtake China as the largest growth market for energy by late 2020s with the country’s energy consumption growing by more than 4.2% per annum, the fastest among all major economies of the world, according to BP Energy Outlook 2018.
Energy Intensity: Energy intensity is a measure of the energy inefficiency of an economy. It is calculated as units of energy per unit of GDP. High energy intensities indicate a high price or cost of converting energy into GDP. Energy efficiency of appliances and buildings, fuel economy of vehicles, better methods and patterns of transportation, capacities and utility of mass transit, energy rationing or conservation efforts may all impact overall energy intensity of a nation.
Energy Use: Energy demand in rural and urban areas is on a steady rise. China and India are amongst the largest energy consumer in the world. The present energy use is mostly in the areas of domestic cooking and lighting, agriculture, transport and industrial sectors. India’s energy basket has a mix of all the resources available including renewables. The largest energy source is coal, followed by petroleum and traditional biomass.
ARDL Bound Test: Autoregressive Distributed Lag Model (ARDL) Bounds testing procedure is a powerful statistical tool in the estimation of level relationships when the underlying property of time series is entirely I(0), entirely I(1) or jointly co-integrated. Bound testing as an extension of ARDL modelling uses F and t- statistics to test the significance of the lagged levels of the variables in a univariate equilibrium correction system when it is unclear if the data generating process underlying a time series is trend or first difference stationary. Here the ARDL model is applied to examine the short and long run association between variables.