Effects of industrial restructuring on carbon reduction: An analysis of Jiangsu Province, China

Abstract

From the perspective of the development stage of China's economy and the context in which industrial restructuring is highly promoted in China's “Twelve Five-Year Plan”, industrial structure adjustment is an effective way to balance economic growth and carbon reduction. The current study analysed the effects of industrial restructuring on carbon reduction in Jiangsu Province. Using input–output analysis, we calculated both direct and indirect carbon emissions in 1997, 2000, 2002, 2005 and 2007. The study aimed to classify Jiangsu's industrial sectors by the carbon reducing potential (CRP), which was indicated both by carbon reducing efficiency (CRE) and by the amount of carbon reduction (ACR), with a 1% decrease in the output of a certain industrial sector. The results indicate that the high CRE of a certain sector might be due to its high direct carbon intensity, indirect carbon intensity or high economic status. Based on the varying contexts, corresponding policy measures were provided. Moreover, export carbon emissions were abundant in sectors with the highest CRE, indicating the production of emissions due to consumption elsewhere.

Introduction

As the most rapidly growing developing country, China consumes a large amount of energy. Between 2000 and 2009, energy consumption in China increased from 1455.31 million to 3066 million tons of standard coal equivalent [1]. Despite not being forced to reduce carbon emissions in the Kyoto Protocol, because China is one of the international economic powers and the biggest emitter of carbon, the country is obliged to kerb its emissions. In its 17th “Fifth Plenary Session,” the party clearly announced that, faced with the contradiction between increasing requirements for energy and environmental pressure, China must enhance crisis awareness, save energy, promote more environmentally friendly consumption practices and strive to transform from high-carbon to low-carbon energy use [2].

Unlike developed countries, industry still serves as the biggest contributor to China's national economy, with the percentage of heavy industry constantly increasing. Most of the heavy industries consume a considerable amount of energy and grow rapidly while lagging far behind developed countries in terms of energy efficiency [3], thereby causing a large amount of carbon emissions. Between 1980 and 2005, industry end-use energy consumption constituted 60% [4] of all end-use energy consumption, and the carbon emissions of industries accounted for 65% [5] of the total emissions from energy use. Considering the current economic development and social situation in China, Chinese industries will continue to depend on fossil fuels for a long time, which will likely lead to substantial carbon emissions. To avoid the negative influence of emission reduction on economic growth, the Chinese government faces the difficult issue of balancing economic growth and emission reduction.

Industrial restructuring is an effective way to solve the aforementioned dilemma. Industrial structure is significant for industrial development because reasonable industry structure enhances specialisation and cooperation between industries, increases outputs, increases the rate of innovation and enhances competitiveness. In contrast, unreasonable industrial structure negatively affects industrial development [6]. Zhang and Ren [7] suggested that adjusting industrial structures would directly affect carbon emissions. Moreover, the national “Twelve Five-Year Plan” also aims to reduce energy intensity by 20% through industrial restructuring. Thus, from the perspective of national policy and considering China's current situation, it is necessary to study the effects of industrial restructuring on carbon reduction.

This study uses input–output analysis to investigate the effects of industrial restructuring on carbon emissions. Leontief devised input–output analysis in the 1930s and applied it to study links between the economy and the environment. Input–output analysis has long been considered a useful technique to combine energy use and ultimate demand [8]. One important function of input–output analysis is its ability to quantify the interdependence between industries [9]; therefore, input–output analysis is quite appropriate for industrial structure analysis. Moreover, input–output analysis overcomes the limitation of merely calculating the direct emissions of a certain industrial sector and instead provides a way to deduce indirect carbon emissions created in other sectors that are linked to the “key” sector [10]. Therefore, the carbon emissions of the complete supply chain can be determined, thereby revealing the actual sources of carbon emissions to policy-makers.

Recently, there has been an increasing trend of using input–output analysis to examine environmental issues, especially carbon emissions. A number of studies have applied input–output analysis to analyse carbon emissions of industrial sectors. Liu et al. used input–output analysis to calculate both the direct and total energy intensity of 52 industrial sectors in China [11]; Munksgaard and Pedersen used input–output analysis to calculate both the direct and indirect carbon emissions of all industries in Denmark [12]; Sanchez-Choliz and Duarte used single-region input–output analysis to analyse the carbon emissions of all industries in Spain in international trade and calculated the direct, indirect, import and export carbon emissions of each industry [13]; and Su et al. concluded that in sector disaggregation, emission levels of approximately 40 sectors are sufficient to capture the overall share of emissions embodied in exports, while in spatial disaggregation, the use of emission levels in a greater number of regions produces more detailed results [14], [15].

The focus of our research is to examine the impact of changes in the output of a certain industry on total carbon emissions (known in the literature as the multiplier effect). Previous research has been conducted in this field. Tarancon and Del Rio summarised a series of studies that analysed the impact of changes in the final demand of different sectors on energy use or pollutant generation [10]. Cellura et al. assessed the energy and environmental impacts associated with the consumptions of the Italian households in the period of 1999–2006 by combining the energy and environmental input–output model with the Life Cycle Assessment methodology [16]. Parikh et al. analysed both direct and indirect carbon emissions of the Indian economy by sectors and due to final consumption using the input–output model [17]. Mu et al. studied industrial interrelationships in the electricity consumption chain on the basis of the input–output table of that national economy and applied an electricity demand multiplier to identify sectors with large electricity consumption [18]. Alcantara et al. studied forward and backward linkage effects to identify key electricity consumption sectors in Spain by combining the input–output approach with a sectoral-focused study [19]. Cellura et al. combined an energy and environmental input–output analysis with structural decomposition analysis, evaluated the energy use and air emissions of the productive sectors required to meet Italian household final demand in the period 1999–2006 and identified the sources of variations in energy and environmental indicators [20]. Yuan et al. estimated that the decrease in final demand caused by the Global Financial Crisis could lead to a 7.33% decrease in GDP and a 9.21% reduction in total energy consumption, while the increase of final use caused by the Chinese government's stimulus plan could lead to a 4.43% increase in GDP per year and an energy consumption increase of 1.83% [21]. Kahrl and Holst examined economic growth in China and analysed the impact of changes in final demand on energy consumption [22]. Gould and Kulshreshtha applied input–output analysis to examine the impact of changes in final demand on provincial energy use [23]. Karkacier and Goktolga studied the impact of changes in the final demand of the agricultural sector on total energy consumption using input–output analysis [24].

However, within this group of investigations, few studies have combined the final demand multiplier with the output value of each sector, which is significant in China. Unlike developed countries, the economic growth in China is still increasing to meet the needs of its citizens. Therefore, while trying to reduce carbon emissions, China cannot sacrifice its economic development. Whether an industrial sector with a high final demand carbon emission multiplier should be controlled is unclear. Moreover, in the process of industrial restructuring, the amount of carbon reduction caused by lowering the economic status of a certain sector should also be calculated, as it directly indicates the effects of reduction.

In our research, these issues are considered, and a case study of China's major province (Jiangsu) is provided. China contains 23 provinces, 4 municipalities directly under the control of the central government, 5 autonomous regions and 2 special administrative regions. Among the 34 regions, Jiangsu Province plays an important role in the national economy. The permanent population in Jiangsu is now 78.66 million, ranking it fifth among all provinces. Jiangsu significantly contributes to national GDP growth. The GDP of Jiangsu is consistently in the top three of all provinces, and its proportion in the national economy is still increasing, reaching 10.4% in 2010 [25]. Additionally, at the expense of rapid economic growth, Jiangsu consumes a large amount of fossil fuel. In 2009, Jiangsu Province consumed 237.09 million tons of standard coal equivalent, ranking it fourth among the 34 regions in China [26]. Thus, the rapid economic growth and extensive consumption in the Jiangsu Province could be representative of the characteristics of China; therefore, carbon reduction in Jiangsu is of great significance to China as a whole.

The rest of the paper is organised as follows. The method of input–output analysis is described in Section 2, followed by a description of data sources in Section 3. Section 4 discusses the results from the analysis, and conclusions are provided in Section 5.