Trend Prediction and Decomposed Driving Factors of Carbon Emissions in Jiangsu Province during 2015–2020

Abstract

According to the economic and energy consumption statistics in Jiangsu Province, we combined the GM (1, 1) grey model and polynomial regression to forecast carbon emissions. Historical and projected emissions were decomposed using the Logarithmic Mean Divisia Index (LMDI) approach to assess the relative contribution of different factors to emission variability. The results showed that carbon emissions will continue to increase in Jiangsu province during 2015-2020 period and cumulative carbon emissions will increase by 39.5487 million tons within the forecast period. The growth of gross domestic product (GDP) per capita plays the greatest positive role in driving carbon emission growth. Furthermore, the improvement of energy usage efficiency is the primary factor responsible for reducing carbon emissions. Factors of population, industry structure adjustment and the optimization of fuel mix also help to reduce carbon emissions. Based on the LMDI analysis, we provide some advice for policy-makers in Jiangsu and other provinces in China.

Analysis of Energy-Related Carbon Emissions in Inner Mongolia, China

“…The empirical results in this paper show that CO 2 emissions in Inner Mongolia showed a significantly increasing trend before 2013. This conclusion is consistent with the results in many previous studies: All-China [11,[13][14][15], Northwestern China [20]; Jiangsu [25], Guangdong transportation sector [22], Chongqing [28], heavy industry [34], and the transportation industry [32]. This means that the influences of a series of strong carbon reduction policies, initiated by the Chinese government during the "Twelfth Five-Year Plan" period, played effective roles in the inhibition of In this study, it was found that the rapid wind power growth and the renewable energy weight increase helped reduce the growth of CO 2 emission.…”

“…In this study, it has been shown that the labor productivity factor, economic structure factor and energy density factor were the main drivers affecting CO 2 emission fluctuations in Inner Mongolia, and this coincides with the finding of many previous studies [11][12][13]15,20,23,32]. Like a few research conclusions on Northwestern China [20] and All-China [14], it was also found that population had a weak positive effect on CO 2 emissions growth, but this finding differed from the conclusion reached by Tang et al [25] where population factors contributed to reduced CO 2 emissions in Jiangsu. In addition, this study found that labor productivity promotion had a strong effect on CO 2 emissions growth in Inner Mongolia, which coincides with the conclusion reached by Lin and Liu [34].…”

“…Secondly, studies performed at the regional/provincial level were all resourceful, including those on Northwest China [20], Yunnan [21], Guangdong [22][23][24], Jiangsu [25,26], Shanghai [27], Chongqing [28,29], and Xinjiang [30]. The factors found to have the greatest effect on carbon emissions were economic activity [20], investment [21], energy density [21,23,24,26], economic growth [23,24], export [21], population scale [24], energy efficiency [20], technical progress [24], industrial structure [22,23,26], and energy Sustainability 2019, 11,7008 3 of 20 mix [24,26].…”

The Integrated Approach for Sustainable Performance Evaluation in Value Chain of Vietnam Textile and Apparel Industry

“…Grey prediction is used to predict future values based on realistic data [26]. Tang et al applied the grey model GM (1, 1) to forecast carbon emissions, and the results showed that carbon emissions will rise in Jiangsu Province over the period 2015-2020; therefore, a prompt improvement plan is considered carefully to minimize future carbon emissions [27]. Grey forecasting model was employed to forecast future indicators in logistic companies by Yu et al and the empirical results found problems and suggested solutions to enhance competitiveness relative to rivals in global economic uncertainty [28].…”

Ecosystem Services Value Assessment and Uneven Development of the Qingjiang River Basin in China

“…The results show that the original time series presents an index variation law [47]. Therefore, the grey prediction method is used to forecast the ESV of the secondary ecological communities from 2016 to 2035 in the study area [48,49]. The formula is:…”