Decoupling Economic Growth from Carbon Emissions in the Yangtze River Economic Belt of China: From the Coordinated Regional Development Perspective

By inergency On Mar 16, 2024

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4.2.1. Decomposition between Regions

The factors driving carbon emission in YREB are examined from 2005 to 2019 by the Kaya Identity and the LMDI decomposition approach (see Figure 3). As shown in Figure 3A, from 2005 to 2010, the carbon emission of the YREB increased by 1050 Mt, while it just increased by 387 Mt from 2010 to 2015 and 216 Mt from 2015 to 2019. Since 2011, the growth trajectory of carbon emissions has significantly flattened compared to the period from 2005 to 2010. This indicates that in recent years, YREB has actively pursued a sustained reduction in carbon emissions through economic restructuring and the elimination of outdated production capacity. The economic growth effect is the primary factor promoting carbon emissions, while the energy intensity effect is crucial in restraining it. This is primarily attributed to the reduction in economic activity and the heightened suppression of energy intensity. The coefficient of emission factor turns from positive to negative, indicating that the YREB produced lower carbon emissions with the equivalent energy use. The evidence supports the view that technological advances have likely contributed to energy efficiency improvements. The effect of population on carbon emissions is always positive and stable with a minor value. This suggests that the increase in population scale will contribute to carbon emissions, but the contribution is smaller than other factors. In conclusion, for the YREB as a whole, promoting decoupling and increasing the energy intensity of each economic sector will be the most effective way to control carbon emissions.

Figure 3B illustrates the impact on carbon emission of different driving factors in CDP, MRP, and WLP. For CDP, the economic growth effect is the predominant factor driving carbon emissions, contributing 61% to 50%. Simultaneously, the energy intensity effect plays a crucial role in curbing carbon emissions, with its contribution escalating from 27% to 39%. The emission factor inhibited carbon emission throughout the study period. The population-scale effect promoted carbon emissions with a decreasing contribution. This suggests that economically developed provinces have shifted their development focus towards the quality of economic growth. Simultaneously, the growing population in first-tier cities, while exhibiting a declining trend in carbon emissions per capita, continues to exert a substantial influence on the overall increase in carbon emissions.

For MRP, the economic growth effect takes the lead in promoting carbon emissions, with its contribution decreasing from 65% to 54%. The energy intensity effect serves as a significant factor in restricting carbon emissions, contributing 24% to 41%. The emission factor effect inhibited carbon emission (contributing 0.01%) from 2011 to 2015 and promoted it during other periods. The population-scale effect promotes the growth of carbon emissions, contributing 1%.

For WLP, the economic growth effect stands out as the primary driver of carbon emissions, and the contribution is decreasing from 68% to 46%. The energy intensity effect proves to be the most significant factor in constraining carbon emissions, contributing 24% to 40%. The emission factor effect promoted carbon emission (contributing 6%) from 2006 to 2010 and inhibited it during other periods. The population-scale effect suppressed carbon emissions from 2006 to 2010 and promoted carbon emissions post-2010 (contributing 3%).

We examined the rationales underlying the influence of each factor. Regarding the economic growth effect, the contribution to increased carbon emissions is diminishing across all regions. This trend suggests a gradual decoupling of economic growth from carbon emissions, aligning with the results obtained from the decoupling index analysis. In terms of the emission factor effect, it is constantly negative in CDP, while in other regions, it is trending from positive to negative. This indicates that CDP have a cleaner energy structure than MRP and WLP. In terms of the population-scale effect, the CDP population scale has a greater contribution to carbon emissions compared to MRP and WLP. It is due to the significant population pressure in CDP. The average per capita parkland area in CDP in 2019 was 12.58 m² per person, lower than in MRP (13.28 m² per person) and WLP (14.73 m² per person).

4.2.2. Decomposition within Regions

Figure 4A illustrates the decomposition results for carbon emissions in CDP. The carbon emissions increased from 568 Mt in 2005 to 1378 Mt in 2019. The positive factors involve the economic growth effect and the population-scale effect. The economic growth effect contributed 60% to the carbon emission increase in 2006–2010. However, the impact of the economic growth effect was diminishing, and the contribution decreased to 50% in 2011–2015 and 2016–2019. Rapid economic growth generates the negative environmental externalities of increasing carbon emissions, and the negative externality decreases with the transition to green development. The negative factors involve the emission factor effect and energy intensity effect. The emission factor effect is negative and may mean the improvement of energy structure. The energy intensity effect is the most significant deceleration factor and contributed 40% to carbon emission reduction in 2011–2015. With technological progress and increased investment in energy conservation, the inhibitory effect on carbon emissions has increased. The contribution of the emission factor effect and the population-scale effect to carbon emission increase was about 10%, respectively.

Figure 4B illustrates the impact on carbon emission of different driving factors in Shanghai city, Jiangsu province, and Zhejiang province. For Jiangsu province, the contribution of economic growth effect exceeded 50% in the study period, and the population-scale effect contributed less than 10% to carbon emissions. The contribution of the emission factor effect and energy intensity effect is increasing. For Shanghai city, the economic growth effect is the main driving factor (contributing from 39% to 51%), while energy intensity is the main inhibiting factor (contributing from 26% to 43%). The contribution of the population-scale effect is decreasing from 23% to 1%. For Zhejiang province, the contribution of the emission factor effect was only 0.1% in 2006–2010 and increased to 22% in 2016–2019. The energy intensity effect is the most significant factor restraining carbon emissions. The contribution of the economic growth effect was decreasing from 59% to 39%. The contribution of the population-scale effect remained at about 12%.

The results of this study not only align with existing research, such as Huang et al. [75]’s examination of carbon emission growth factors in the Yangtze River Delta region (Shanghai city, Jiangsu province, and Zhejiang province) using the LMDI method, but also introduce novel perspectives. In concordance with prior findings, both studies emphasize economic growth as a primary driver of carbon emissions, highlighting energy intensity as a key deceleration factor. However, our study goes beyond these agreements by shedding light on the sustained significance of economic growth in contributing to carbon emission. Despite the fact that all provinces have proposed strict green development goals and achieved good results, there is the lingering issue of economic growth’s strong reliance on energy consumption.

Analyzing the different roles of influencing factors, it is evident that Jiangsu province had higher total carbon emissions than Zhejiang province and Shanghai city during the study period. This can be attributed to two main factors. Firstly, Jiangsu province boasts the highest GDP among the four provinces, reaching CNY 9.96 trillion in 2019, surpassing Zhejiang province by CNY 3.73 trillion, equivalent to Shanghai’s GDP in the same year. Additionally, the total energy consumption of Jiangsu province far exceeds that of Shanghai city and Zhejiang province. In 2019, the total energy consumption of Jiangsu province was 2.8 times that of Shanghai city and 1.5 times that of Zhejiang province.

In terms of the emission factor effect, only Jiangsu province is positive among the three provinces. It should be noted that the emission factor effect promoted the carbon emissions in Jiangsu province from 2011 to 2019. It indicates that Jiangsu province should pay primary attention to adjusting the energy consumption structure. Similarly, the increase in the contribution of the emission factor effect in Zhejiang province is due to the improvement of energy structure, with the proportion of clean energy sources in total energy consumption in Zhejiang province increasing from 20% in 2006 to 35% in 2019. The increase in clean energy consumption leads to the improvement of energy structure, which generates a more significant inhibitory impact of the emission factor effect in Zhejiang province.

In conclusion, the province with priority for carbon emission reduction in CDP is Jiangsu province. It is crucial to closely monitor both the overall energy consumption and the specific energy consumption structure within Jiangsu province. Effectively managing traditional energy consumption and elevating the share of clean energy sources in total energy consumption stand out as the most impactful strategies for curbing carbon emissions in Jiangsu province.

2.: Middle-rising provinces

Figure 5A illustrates the decomposition results for carbon emissions in MRP. The total in carbon emissions of the MRP is lower than that of the CDP, but the increment is higher than that of the CDP. The economic growth effect is the most influential factor promoting carbon emission in the MRP, and the promoting effect increases first and then decreases. This suggests that economic growth in MRP is gradually decoupling from carbon emissions, which is consistent with the results of the decoupling analysis. The energy intensity effect is the most important factor restraining carbon emissions. The emission factor effect promoted carbon emission in 2005–2010, inhibited carbon emission in 2010–2015 (only 0.1), and then promoted carbon emission from 2015 to 2019. Compared with the CDP, it indicates that the energy structure of the MRP needs to increase the use of clean energy. The population-scale effect plays a role in promoting carbon emissions, but it is only about 10, indicating that the population scale of the MRP has a small impact on carbon emissions.

Figure 5B illustrates the impact on carbon emission of different driving factors in Anhui province, Hubei province, Jiangxi province, and Hunan province. For Anhui province, the economic growth effect dominates, contributing about 60%, while the energy intensity effect follows, rising from 21% to 38%. The population-scale effect inhibited carbon emissions during 2006–2010 and promoted it later with an increasing contribution. For Hubei province, the contribution of energy intensity to carbon emission suppression increased from 21% to 41%, slightly lower than the promotion of economic growth (48%) during 2016–2019. The population-scale effect consistently promoted carbon emissions, but contributed only from 0.49% to 3.39%. In Jiangxi province, the economic growth effect’s significant contribution decreased from 67% to 59%. The energy intensity effect’s inhibitory effect rose to 33%, and the population-scale effect consistently promoted, contributing from 0.42% to 2.67%. For Hunan province, the economic growth effect remained the primary contributor (47% to 65%), and energy intensity’s inhibitory effect rose from 24% to 45%. These results are consistent with previous studies [80].

Examining the rationales underlying the influence of each factor, the suppressive effect of the emission factor effect is enhanced in Anhui province and Jiangxi province, while the contrary is observed in Hunan province. This is attributed to the rising share of clean energy sources in the overall energy consumption in Jiangxi province, increasing from 15% in 2007 to 30% in 2019. Similarly, it suggests that the energy structure of Anhui province has been improved from 2006 to 2019. In 2019, the proportion of clean energy sources in total energy consumption in Anhui province is 35%. Conversely, energy restructuring in Hunan province is proceeding slowly. The same energy consumption will generate more GDP as Hunan province increases its technological progress and investment in energy efficiency. In contrast, Hunan province spent CNY 24.2 billion on energy conservation and environmental protection in 2019, compared with CNY 19.4 billion in Jiangxi province. The share of clean energy sources in total energy consumption was only 21% in Hunan province and 30% in Jiangxi province. Regarding the energy intensity effect, its contribution in Hubei province is significantly enhanced. This increase can be primarily attributed to a rise in expenditure on energy conservation and environmental protection, reaching CNY 31.2 billion in 2019—eight times higher than the CNY 3.7 billion recorded in 2007.

In general, MRP have achieved some results in controlling carbon emissions by increasing spending on energy efficiency and environmental protection. The following major measures for carbon emission reduction should focus on how to accelerate the adjustment of energy structure.

3.: Western less-developed provinces

Figure 6A illustrates the decomposition results for carbon emissions in WLP. The carbon emissions of WLP increased from 530 Mt in 2005 to 918 Mt in 2019. The total carbon emissions and carbon emission increment of WLP are lower than those of CDP and MRP. The effect of economic growth is the most influential factor promoting carbon emission in the WLP, with the promotional impact initially increasing and then decreasing. This suggests that economic growth in WLP is gradually decoupling from carbon emissions, aligning with the results of the decoupling analysis. The energy intensity effect remains the most crucial factor in restraining carbon emissions. The population-scale effect in WLP suppressed carbon emissions during 2005–2010 and began to promote it post-2010. The emission factor effect promoted carbon emissions during 2005–2010 and began to inhibit it post-2010. It shows that the energy structure of WLP has improved.

Figure 6B illustrates the impact on carbon emission of different driving factors in Chongqing city, Yunnan province, Sichuan province, and Guizhou province. For Chongqing city, the economic growth effect drives carbon emissions significantly (contributing from 64% to 40%), while energy intensity effect increasingly inhibits it (from 23% to 47%). It illustrates that energy efficiency in Chongqing city has improved with technological progress. The emission factor effect initially promotes emissions (2006–2010) but later inhibits it, with a growing effect (from 0.95% to 6.52%). Sichuan’s economic growth effect promotes carbon emissions (from 60% to 42%), countered by an increasingly influential energy intensity effect (from 21% to 44%). The emission factor effect promoted carbon emission (contributing 16%) in 2006–2010 and inhibited it during other periods, and the inhibition effect increased from 1% to 15%. The population-scale effect shifts from suppression to promotion. In Yunnan province, the economic growth effect dominates carbon emissions (from 41% to 67%), while the energy intensity effect increasingly restrains it (from 22% to 46%). The emission factor initially promotes, then inhibits it, and the population-scale effect shifts from suppression to promotion. Guizhou province sees rising influence of the economic growth effect (71% in 2016–2019). Emission factor effects vary across periods, and the population-scale effect contributes to carbon emission growth (from 1% to 4%). These results are consistent with previous study [81].

Examining the rationales underlying the influence of each factor, in terms of emission factor effects, the inhibitory effect of emission factor effects on carbon emissions has been increasing in Chongqing city and Sichuan province, while the contrary has been observed in Yunnan province. This is mainly due to the increase in the proportion of clean energy sources in total energy consumption in Chongqing city, from 19% in 2007 to 32% in 2019. Similarly, this shows that the measures to adjust the energy structure of Sichuan province are effective. As a high-quality clean energy base in China, Sichuan province has been actively promoting its development as a national clean energy demonstration province since the 13th Five-Year Plan. Clean energy consumption in Sichuan province increased to 32% of total energy consumption in 2019. However, in 2019, clean energy sources accounted for only 20% of total energy consumption in Yunnan province.

Regarding the economic growth effect, Guizhou province exhibits a significantly higher contribution to carbon emissions compared to other provinces and cities. It shows that the economic development of Guizhou province depends on energy consumption. Guizhou province is in the southwestern inland with large reserves of mineral resources and a complete species of mineral resources. Mineral resources are an important component of Guizhou’s GDP. In 2019, the added value of industry above the scale in the province increased by 9.6% compared with the previous year, and by category, the added value of the mining industry increased by 13.7%, which is ranked first (data from the Statistical Bulletin of National Economic and Social Development of Guizhou province in 2019).

In general, WLP carbon reduction mainly depends on Yunnan province and Guizhou province. Yunnan province should pay attention to energy efficiency and energy structure, and actively increase the proportion of clean energy sources in total energy consumption. Guizhou province is still significantly dependent on industry, despite its current GDP dominated by tertiary industries.

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