Estimating the Contribution of Industry Structure Adjustment to the Carbon Intensity Target: A Case of Guangdong

Abstract

Industry structure adjustment is an effective measure to achieve the carbon intensity target of Guangdong Province. Accurately evaluating the contribution of industry structure adjustment to the carbon intensity target is helpful for the government to implement more flexible and effective policies and measures for CO 2 emissions reduction. In this paper, we attempt to evaluate the contribution of industry structure adjustment to the carbon intensity target. Firstly, we predict the gross domestic product (GDP) with scenario forecasting, industry structure with the Markov chain model, CO 2 emissions with a novel correlation mode based on least squares support vector machine, and then we assess the contribution of industry structure adjustment to the carbon intensity target of Guangdong during the period of 2011-2015 under nine scenarios. The obtained results show, in the ideal scenario, that the economy will grow at a high speed and the industry structure will be significantly adjusted, and thus the carbon intensity in 2015 will decrease by 25.53% compared to that in 2010, which will make a 130.94% contribution to the carbon intensity target. Meanwhile, in the conservative scenario, the economy will grow at a low speed and the industry structure will be slightly adjusted, and thus the carbon intensity in 2015 will decrease by 23.89% compared to that in 2010, which will make a 122.50% contribution to the carbon intensity target.

Toward a Sustainable Low-Carbon China: A Review of the Special Issue of “Energy Economics and Management”

“…Most papers in this Special Issue show great interest in the CO 2 emissions [1,7,9,11,12] and the carbon intensity [8,13]. Some of these studies use the conventional regression to analyze CO 2 emissions [1,8,13], but the downside of this method is that conventional regressions cannot solve the problems of multiple inputs/outputs in these models.…”

“…Some of these studies use the conventional regression to analyze CO 2 emissions [1,8,13], but the downside of this method is that conventional regressions cannot solve the problems of multiple inputs/outputs in these models. Therefore, a centralized DEA approach was used to determine the optimal path for controlling CO 2 emissions at the sector level for each province in China, which can avoid positive shadow prices on undesirable outputs [9].…”

“…By using the Markov chain model, least squares support vector machine, and scenario prediction, the obtained results show, in the ideal scenario, that carbon intensity in 2015 will be 25.53% less than in 2010, making a 130.94% contribution to the carbon intensity target. Meanwhile, in the conservative scenario, carbon intensity in 2015 will be 23.89% less than in 2010, making a 122.50% contribution to the carbon intensity target [8].…”

The Economy-Carbon Nexus in China: A Multi-Regional Input-Output Analysis of the Influence of Sectoral and Regional Development

“…The development of the goods trade and the export-oriented processing industry has promoted economic growth in Guangdong. In 2010, Guangdong's (R19) GDP reached 4.6 billion Yuan at the current price, accounting for 11% of the national total [64]. At the sectoral level, there are 12 sectors (S3, S4, S5, S6, S7, S8, S9, S10, S11, S13, S14, S15) out of the total of 17 sectors with high REICs above 1.000.…”

“…As shown in the map (a) in Figure 2, Guangdong (R19) is located in the southern coastal area that was one of the first regions to open up in China. Due to its proximity to the special administrative regions of Hong Kong and Macao, Guangdong (R19) has a high level of openness [64]. The development of the goods trade and the export-oriented processing industry has promoted economic growth in Guangdong.…”