In 2010, China was responsible for nearly 20 percent of global energy use and 25 percent of energy-related carbon dioxide (CO2) emissions. Unlike most countries, China's energy consumption pattern is unique because the industrial sector dominates the country's total energy consumption, accounting for about 70 percent of energy use and 72 percent of CO2 emissions in 2010. For this reason, the development path of China's industrial sector will greatly affect future energy demand and dynamics of not only China, but the entire world. A number of analyses of historical trends have been conducted, but careful projections of the key factors affecting China's industry sector energy use over the next decade are scarce. This study analyzes industrial energy use and the economic structure of the Chinese manufacturing sector in detail. First, the study analyzes the energy use of and output from 18 industry sub-sectors. Then, retrospective (1995-2010) and prospective (2010-2020) decomposition analyses are conducted for these industrial sectors in order to show how different factors (production growth, structural change, and energy intensity change) influenced industrial energy use trends in China over the last 15 years and how they will do so over the next 10 years.
The historical analysis results show that top energy-consuming subsectors such as smelting and pressing of ferrous metals, raw chemical materials and chemical products manufacturing, and non-metallic mineral product manufacturing use more energy per value added and comprise a large share of Chinese manufacturing primary energy use while having a much lower share of total manufacturing value added in 2010. In contrast, the electric and electronic equipment manufacturing, food, beverage and tobacco industry, and machinery manufacturing accounted for more than 1/3 of manufacturing value added while only consuming 8 percent of total Chinese manufacturing primary energy in 2010.
The decomposition analysis shows that both energy intensity reduction and changes in structure contributed to the reduction in energy use in Chinese manufacturing during the periods 1995-2000 and 2005-2010. In all years, the activity effect increased overall energy use. Also in all years, the intensity effect reduced overall energy consumption, providing a counter-balance to the increased energy use due to increased activity. The structural effect also reduced overall energy consumption except during the period 2000-2005 when it caused an increase in manufacturing energy use primarily because the share of value added from top energy-intensive sectors like smelting and pressing of ferrous metals from total manufacturing value added increased during this period. The intensity effect during the 10th FYP (2001-2005) is the smallest and slightly decreased primary energy use compared to the other periods with larger intensity effects. This was due to a very small decline in overall manufacturing energy intensity during this period because the energy intensity of some manufacturing subsectors, especially the top five energy-intensive manufacturing subsectors (except smelting and pressing of ferrous metals), either remained relatively steady or even increased in some cases.
The forward looking (prospective) decomposition analyses are conducted for three different scenarios. The three scenarios are defined based on different predicted average annual growth rates (AAGR) for value added for different manufacturing subsectors. The value added AAGRs in scenario 1 are mainly based on those provided by Chinese sources. In scenario 2 the value added AAGRs are based on Oxford Economics forecasts. In scenario 3 the value added AAGRs are based on expert judgment.[1] The analysis for 2010-2020 shows that the activity effect is largest under scenario 1 because of the higher value added AAGRs assumed for manufacturing subsectors under this scenario. The structural effect, however, is largest in scenario 3 because the share of value added of energy-intensive subsectors such as smelting and pressing of ferrous metals and non-metallic mineral products sectors in total manufacturing value added in 2015 and 2020 are lower in scenario 3 compared to the other two scenarios.
The scenario analysis indicates that if China wants to realize structural change in the manufacturing sector by shifting from energy-intensive and polluting industries to less energy-intensive industries, the value added AAGRs to 2015 and 2020 should be more in line with those shown in scenario 3. The assumed value added AAGRs for scenario 3 are relatively realistic and are informed by possible growth that is foreseen for each subsector. Such structural change is also a result of shifts in demand for manufactured products. The government can influence demand for manufactured products indirectly, but only to some extent, and generally only temporarily. Hence, in addition to government policies in the past, the industrial structural change in China we have analyzed in this study are also caused by broad macroeconomic trends such as where a country is on the development path, emerging demand trends, and the country's economic comparative advantage in meeting different types of demand.
The results of this study will allow policy makers to quantitatively compare the level of structural change in the past and in the years to come and adjust their policies if needed to move towards the target of less energy-intensive industries. The scenario analysis shows the structural change achieved through different paths and helps to understand the consequences of supporting or limiting the growth of certain manufacturing subsectors from the point of view of energy use and structural change. The results point out the industries that have the largest influence in such structural change.
The expert judgment is that of the authors, Bob Taylor (formerly of World Bank), and colleagues at China's Energy Research Institute and is based on the sources of information used for scenarios 1 and 2 as well as their knowledge of Chinese policies and discussions with experts from a number of Chinese industrial associations.
}, year = {2013}, month = {01/2013}, publisher = {Lawrence Berkeley National Laboratory}, address = {Berkeley}, }