A comparison analysis of the decoupling carbon emissions from economic growth in three industries of Heilongjiang province in China

Analysis on influencing factors of carbon emissions from China's pulp and paper industry and carbon peaking prediction

China's pulp and paper industry (CPPI) has been always the main carbon emission source in recent years. However, the analysis on influencing factors of carbon emissions from this industry is insufficient. To address the issue, the CO2 emissions from CPPI are estimated in the period of 2005-2019, the driving factors of CO2 emissions are investigated by the logarithmic mean Divisia index (LMDI) method, the decoupling state of economic growth and CO2 emissions is determined by Tapio decoupling model, and finally, future CO2 emissions are predicted under four scenarios by the STIRPAT model to explore the potential of carbon peaking. The results show that CPPI exhibits a rapid increase and a fluctuating downward trend in CO2 emissions during the period of 2005-2013 and 2014-2019, respectively. The main promoting and inhibiting factors to the increase of CO2 emission are per capita industrial output value and energy intensity, respectively. There are five decoupling states of CO2 emissions and economic growth during the study period, and the CO2 emissions exhibit a weak decoupling state with the industrial output value growth in most years of the study period. It is very difficult to realize the carbon peaking goal by 2030 under the baseline and fast development scenarios. Therefore, efficient low carbon and strong low-carbon development policies are necessary and urgent for the realization of carbon peaking goal and the sustainable development of CPPI.

Decomposition, decoupling, and future trends of environmental effects in the Beijing-Tianjin-Hebei region: A regional heterogeneity-based analysis

The green growth of Beijing-Tianjin-Hebei (BTH) urban agglomeration plays a leading and exemplary role in overcoming internal resource restrictions, addressing climate change, and supporting China's high-quality growth. From the standpoint of pollution reduction and carbon reduction, this paper first conducts a comprehensive evaluation of the environmental impact based on combined weighting technique. The Logarithmic Mean Divisia Index (LMDI) model is used to decompose the environmental impact drivers in distinct areas. A decoupling effort index is further constructed to measure the effect of various efforts on the decoupling of economic growth and environmental impact, the improved grey Markov model is applied to predict the future trend of regional decoupling efforts. The results of empirical analysis based on data of the BTH region during 2011-2018 show that: 1) the environmental impact index of Beijing is the lowest followed by Tianjin and Hebei; 2) environmental regulation exerts the most significant impact on reducing environmental pressure in Beijing while technology progress and energy intensity have the most significant effect on easing environmental pressure in Tianjin; 3) strong decoupling efforts have been found in Beijing, Tianjin and Hebei, however, such effect is more significant in Beijing; 4) Beijing's decoupling state is mostly driven by regulatory effect, intensity effect, and scale effect, while Tianjin and Hebei's decoupling states are primarily driven by improvements in environmental regulation and energy intensity; 5) according to the forecast outcome of the improved grey Markov technique, a state of strong decoupling effort will be maintained in the BTH area by 2025, and the decoupling effort index in Beijing will remain the highest while the index in Hebei will remain the lowest.

Decomposition and Decoupling Analysis of Carbon Emissions in Xinjiang Energy Base, China

China faces a difficult choice of maintaining socioeconomic development and carbon emissions mitigation. Analyzing the decoupling relationship between economic development and carbon emissions and its driving factors from a regional perspective is the key for the Chinese government to achieve the 2030 emission reduction target. This study adopted the logarithmic mean Divisia index (LMDI) method and Tapio index, decomposed the driving forces of the decoupling, and measured the sector’s decoupling states from carbon emissions in Xinjiang province, China. The results found that: (1) Xinjiang’s carbon emissions increased from 93.34 Mt in 2000 to 468.12 Mt in 2017. Energy-intensive industries were the key body of carbon emissions in Xinjiang. (2) The economic activity effect played the decisive factor to carbon emissions increase, which account for 93.58%, 81.51%, and 58.62% in Xinjiang during 2000–2005, 2005–2010, and 2010–2017, respectively. The energy intensity effect proved the dominant influence for carbon emissions mitigation, which accounted for −22.39% of carbon emissions increase during 2000–2010. (3) Weak decoupling (WD), expansive coupling (EC), expansive negative decoupling (END) and strong negative decoupling (SND) were identified in Xinjiang during 2001 to 2017. Gross domestic product (GDP) per capita elasticity has a major inhibitory effect on the carbon emissions decoupling. Energy intensity elasticity played a major driver to the decoupling in Xinjiang. Most industries have not reached the decoupling state in Xinjiang. Fuel processing, power generation, chemicals, non-ferrous, iron and steel industries mainly shown states of END and EC. On this basis, it is suggested that local governments should adjust the industrial structure, optimize energy consumption structure, and promote energy conservation and emission reduction to tap the potential of carbon emissions mitigation in key sectors.