What causes the change in energy demand in the economy?

Abstract

This paper proposes a simple and theoretically clear approach to the estimation of technological change in a multisector general equilibrium framework. This study employs the Multiple Calibration Decomposition Analysis (MCDA) to evaluate technological change that is responsible for changes in energy use and carbon dioxide emissions in the Japanese economy in the oil crises period from 1970 to 1985. The MCDA serves as an elementary way of separating structural change due to technological change from that due to price substitution effects, capturing the interdependence among economic sectors. The empirical result provides a better understanding of the effects on the economy of technological change in that significant period.

Nexus between energy consumption, information and communications technology, and economic growth: An enquiry into emerging Asian countries

“…In relation with energy–economy–technology nexus, Okushima and Tamura (2010) utilize Multiple Calibration Decomposition Analysis for evaluating technological change responsible for EN and CO 2 emissions variations in Japan in the oil crises period from 1970 to 1985 and the results offer a better insight of the effects of technological change on the economy in that significant period; Tang and Tan (2013) analyze the interrelationship of electricity consumption and economic growth, energy prices, and technology innovation in Malaysia over the period 1970–2009 by using Autoregressive Distributed lag (ARDL) bounds testing approach and the results illustrate cointegration of electricity consumption and its determinants where the income effects electricity consumption positively, while energy prices and technology innovation posit negative effect on it over the long run; Sohag, Begum, Abdullah, and Jaafar (2015) examine the interdependence of EN, technological innovation, economic growth, and trade openness in Malaysia by using an ARDL bounds testing approach for the period 1985–2012 and the findings show that increasing GDP per capita and trade openness cultivate a rebound effect of technological change on EN; Ishida (2014) estimates the long‐run relationship between energy consumption, ICT, and economic growth in Japan by applying ARDL bounds testing approach on two separate multivariate models concurrent to the production function and the energy demand function with ICT investment as an explanatory variable, over the period 1980–2010 and the results unveil the existence of a long‐run stable relationship for both production function and energy demand function; Cantore, Cali, and te Velde (2016) evaluate the linkage of energy efficiency and economic performance at the microlevel (total factor productivity) and macrolevel (economic growth) by using sample data of 29 developing countries of period 2000–2005 and the findings confirm the association of lower levels of energy intensity with higher total factor productivity for most of the countries, whereas the situation is similar at macrolevel pointed by cross‐country evidence. Betz et al (2019) surveyed readers of Science , Nature , and Harvard Business Review for their views about the advances in technology and business over the next 35 years and conclude that combination of medical advances with developments in artificial intelligence will have the greatest impact on business and society, and the progress in energy with sustainable materials shall further enhance this development.…”

China's Energy Consumption in the New Normal

“…This method is used to evaluate the drivers of energy consumption changes in a country. For example, SDA has been used to estimate the drivers of energy consumption changes in Japan during 1970–1985 (Okushima & Tamura, ), in the U.S. during 1997–2002 (Weber, ), in Brazil during 1970–1996 (Wachsmann et al, ), and in India during 1973–1991 (Mukhopadhyay & Chakraborty, ). SDA is also often used to evaluate the drivers of greenhouse gas emission changes caused by energy consumption.…”

Investigating the Driving Forces of Energy Intensity Change in Malaysia 1991-2010: A Structural Decomposition Analysis

“…For Brazil, Wachsmann et al (2009) revealed that the production structure for 26 years was among the main factors for the increase in energy use, i.e., not energy intensity. Conversely, for the USA (13 years; 1972-1986) and Japan (15 years; 1970-1985), the authors revealed that production structure was the most responsible factor for energy use decline (OTA, 1990;Okushima and Tamura, 2010). In Malaysia, the production structure factor is the main driver in increasing Malaysia's energy intensity, which can be explained by Malaysia's manufacturing sector being more capital intensive, with an increasing number of heavy industries guided by its three phases of the IMPs.…”