Is China's economic growth decoupled from carbon emissions?

Elucidating the complex mechanism between urbanization, economic growth, carbon dioxide emissions is fundamental necessary to inform effective strategies on energy saving and emission reduction in China. Based on a balanced panel data of 31 provinces in China over the period 1997-2010, this study empirically examines the relationships among urbanization, economic growth and carbon dioxide (CO2) emissions at the national and regional levels using panel cointegration and vector error correction model and Granger causality tests. Results showed that urbanization, economic growth and CO2 emissions are integrated of order one. Urbanization contributes to economic growth, both of which increase CO2 emissions in China and its eastern, central and western regions. The impact of urbanization on CO2 emissions in the western region was larger than that in the eastern and central regions. But economic growth had a larger impact on CO2 emissions in the eastern region than that in the central and western regions. Panel causality analysis revealed a bidirectional long-run causal relationship among urbanization, economic growth and CO2 emissions, indicating that in the long run, urbanization does have a causal effect on economic growth in China, both of which have causal effect on CO2 emissions. At the regional level, we also found a bidirectional long-run causality between land urbanization and economic growth in eastern and central China. These results demonstrated that it might be difficult for China to pursue carbon emissions reduction policy and to control urban expansion without impeding economic growth in the long run. In the short-run, we observed a unidirectional causation running from land urbanization to CO2 emissions and from economic growth to CO2 emissions in the eastern and central regions. Further investigations revealed an inverted N-shaped relationship between CO2 emissions and economic growth in China, not supporting the environmental Kuznets curve (EKC) hypothesis. Our empirical findings have an important reference value for policy-makers in formulating effective energy saving and emission reduction strategies for China.

Effects of trade liberalization on the global decoupling and decomposition of CO2 emissions from economic growth

PDF HTML阅读 XML下载 导出引用 引用提醒 “双碳”目标下闽三角碳排放脱钩状态及驱动机制分析 DOI: 10.5846/stxb202201270257 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点研发计划项目（2016YFC0502902） Decoupling status and driving mechanisms of carbon emissions in the Golden Triangle of Southern Fujian under "carbon peaking and neutrality" goals Author: Affiliation: Fund Project: the National Key R&D Program of China (2016YFC0502902) 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:快速城市化背景下，建设低碳城市群是实现"双碳"目标的最佳方式。在碳排放核算的基础上，使用Tapio脱钩模型和LMDI方法对闽三角以及厦门、漳州和泉州的脱钩状态和碳排放的驱动机制进行了研究。主要结论如下：（1）2005-2017年闽三角碳排放和人均碳排放均持续增加，二者有相同的变化趋势。闽三角的工业中心泉州有最高的碳排放和人均碳排放。发展型城市漳州碳排放最低，但碳排放和人均碳排放增长率均最高。服务型城市厦门碳排放增长率最低。（2）闽三角的脱钩状态逐渐改善，平均脱钩系数为1.03，脱钩状态为扩张性连接。厦门、漳州和泉州的平均脱钩系数分别为0.45、2.70和1.10，3个城市分别以弱脱钩、扩张负脱钩和扩张性连接状态为主。（3）人均GDP和人口规模是闽三角碳排放的正向因素，能源结构和能源强度是负向因素。正向因素的贡献在下降，负向因素的贡献在升高。人均GDP和能源结构分别对漳州和厦门碳排放有最强的促进和抑制效应。能源强度对3个城市碳排放变化的效应不同。（4）人口扩张促进碳排放增加，使碳排放与经济发展无法脱钩。人口规模对闽三角碳减排无脱钩努力。能源结构优化和能源强度下降有助于碳排放与经济发展脱钩，是闽三角碳减排的强脱钩努力和弱脱钩努力。能源强度对泉州碳减排无脱钩努力。优化能源结构是闽三角实现碳减排和"双碳"目标的关键。已经脱钩的厦门宜尽早制定碳达峰行动计划，引领闽三角的碳达峰行动。漳州可通过升级产业结构实现减排。泉州必须提升能源效率才能降低碳排放。 Abstract:Developing low-carbon urban agglomeration is the best way to achieve "carbon peaking" and "carbon neutrality" goals under the background of rapid urbanization. Based on carbon emissions accounting, the Tapio decoupling model and the Logarithmic Mean Divisia Index (LMDI) method are utilized to analyze the decoupling status and driving mechanisms of carbon emissions of the Golden Triangle of Southern Fujian (GTSF), Xiamen, Zhangzhou and Quanzhou. The main findings are as follows:(1) Carbon emissions and carbon emissions per capita both kept increasing from 2005 and 2017, with the same trend. Quanzhou, an industrial center of the GTSF, has the highest carbon emissions and carbon emissions per capita. Zhangzhou, a developing city, has the least carbon emissions but the highest growth rate of carbon emissions and carbon emissions per capita. Xiamen, a service-oriented city, holds the lowest growth rate of carbon emissions. (2) Decoupling status of the GTSF has improved. The average decoupling index of the GTSF is 1.03, and the decoupling status is dominated by the expansive connection. The average decoupling indices of Xiamen, Zhangzhou and Quanzhou are 0.45, 2.70 and 1.10, respectively. The dominant decoupling status of the three cities is weak decoupling, expansive negative decoupling, and the expansive connection, respectively. (3) Gross domestic product (GDP) per capita and population size are positive factors of carbon emissions of the GTSF, while energy structure and energy intensity are negative factors. The contribution of the positive factors is decreasing, while the contribution of the negative factors is increasing. GDP per capita and energy structure have the strongest promoting effects and inhibiting effects on carbon emissions of Zhangzhou and Xiamen, respectively. The effects of energy intensity on carbon emissions of the three cities are different. (4) Population expansion leads to an increase in carbon emissions, which is not conducive to the decoupling of carbon emissions and economic development. Population size has no decoupling efforts on carbon emissions reduction of the GTSF. The optimization of energy structure and the decline of energy intensity contribute to the decoupling of carbon emissions and economic development. Energy structure and energy intensity are the strong decoupling efforts and the weak decoupling efforts of carbon emissions reduction of the GTSF, respectively. Energy intensity has no decoupling efforts on carbon emissions reduction of Quanzhou. For the GTSF, optimizing energy structure is the key to realize carbon emissions reduction and "carbon peaking and neutrality" goals. It is suggested that Xiamen, which has been decoupled, should formulate carbon emissions peaking action plans, and lead the peaking actions of the GTSF. Zhangzhou can achieve carbon emissions reduction by upgrading industrial structure. Carbon emissions reduction of Quanzhou depends on the improvement of energy efficiency. 参考文献 相似文献 引证文献

An analysis of the decoupling relationship between CO2 emission in power industry and GDP in China based on LMDI method

Decoupling sectoral economic output from carbon emissions on city level: A comparative study of Beijing and Shanghai, China

This paper analyzes the changes in carbon dioxide (CO2) emissions related to energy consumption in the Polish agricultural sector between 2000 and 2019. Based on the Logarithmic Mean Divisia Index (LMDI), the changes in agricultural CO2 emissions are viewed in the context of changes in six factors, i.e., CO2 emission intensity, substitution of fossil fuels, penetration of renewable energies, energy intensity, labor productivity and number of employees. The analysis demonstrated that total energy consumption declined over the study period; this was related to a reduction in the intake of energy derived from solid fossil fuels (−1.05%), crude oil (−1.01%), electricity (−4.89%), and heat (−1.37%), and to an increased consumption of natural gas (5.78%) and biofuels (0.82%). Furthermore, it follows from the analysis that changes in CO2 emissions witnessed in that period were consistent with changes in energy consumption levels; this resulted from a negligible transformation of the energy mix (largely determined by fossil fuels). Generally, CO2 emissions declined over the study period at a rate comparable (−0.9%) to that of the reduction in energy consumption (−1.03%). In light of the LMDI method, the reduction in CO2 emissions from fuel consumption in the Polish agricultural sector was mainly driven by a reduction in energy intensity and in employment. Conversely, rapid growth in labor productivity was the key factor in increasing carbon dioxide emissions. Compared to these impacts, changes in other factors (i.e., emission intensity, energy mix and penetration of renewable energies) had an extremely small or marginal effect on the variation in CO2 emissions.

Decoupling economic growth from carbon dioxide emissions in China: A sectoral factor decomposition analysis

The 2008–2009 financial crisis, often referred to as the Great Recession, presented one of the greatest challenges to economies since the Great Depression of the 1930s. Before the financial crisis, and in response to the Kyoto Protocol, many countries were making great strides in increasing energy efficiency, reducing carbon dioxide (CO2) emission intensity and reducing their emissions of CO2. During the financial crisis, CO2 emissions declined in response to a decrease in economic activity. The focus of this research is to study how energy related CO2 emissions and their driving factors after the financial crisis compare to the period before the financial crisis. The logarithmic mean Divisia index (LMDI) method is used to decompose changes in country level CO2 emissions into contributing factors representing carbon intensity, energy intensity, economic activity, and population. The analysis is conducted for a group of 19 major countries (G19) which form the core of the G20. For the G19, as a group, the increase in CO2 emissions post-financial crisis was less than the increase in CO2 emissions pre-financial crisis. China is the only BRICS (Brazil, Russia, India, China, South Africa) country to record changes in CO2 emissions, carbon intensity and energy intensity in the post-financial crisis period that were lower than their respective values in the pre-financial crisis period. Compared to the pre-financial crisis period, Germany, France, and Italy also recorded lower CO2 emissions, carbon intensity and energy intensity in the post-financial crisis period. Germany and Great Britain are the only two countries to record negative changes in CO2 emissions over both periods. Continued improvements in reducing CO2 emissions, carbon intensity and energy intensity are hard to come by, as only four out of nineteen countries were able to achieve this. Most countries are experiencing weak decoupling between CO2 emissions and GDP. Germany and France are the two countries that stand out as leaders among the G19.

Based on mathematical models, in-depth analysis about the interrelationship between agricultural CO2 emission and economic development has increasingly become a hotly debated topic. By applying two mathematical models including logarithmic mean divisia index (LMDI) and Tapio decoupling, this work aims to study the driving factor and decoupling trend for Chinese agricultural CO2 emission from 1996 to 2020. Firstly, the intergovernmental panel on climate change (IPCC) method is selected to estimate the agricultural CO2 emission from 1996 to 2020, and the LMDI model is adopted to decompose the driving factors of agricultural CO2 emission into four agricultural factors including economic development, carbon emission intensity, structure, and labor effect. Then, the Tapio decoupling model is applied to analyze the decoupling state and development trend between the development of agricultural economy and CO2 emission. Finally, this paper puts forward some policies to formulate a feasible agricultural CO2 emission reduction strategy. The main research conclusions are summarized as follows: 1) During the period from 1996 to 2020, China's agricultural CO2 emission showed two stages, a rapid growth stage (1996-2015) and a rapid decline stage (2016-2020). 2) Agricultural economic development is the first driving factor for the increase of agricultural CO2 emission, while agricultural labor factor and agricultural production efficiency factor play two key inhibitory roles. 3) From 1996 to 2020, on the whole, China's agricultural sector CO2 emission and economic development showed a weak decoupling (WD) state. The decoupling states corresponding to each time period are strong negative decoupling (SND) (1996-2000), expansive negative decoupling (END) (2001-2005), WD (2006-2015) and strong decoupling (SD) (2016-2020), respectively.

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.

Provincial-level industrial CO2 emission drivers and emission reduction strategies in China: Combining two-layer LMDI method with spectral clustering

This study investigates ways to effectively reduce carbon dioxide (CO2) emissions in Indonesia’s manufacturing industry, by firm characteristics. It is important to determine the firm characters that have the greatest potential to decrease CO2 emissions. The Logarithmic Mean Divisia Index (LMDI) method is used to decompose CO2 emissions into the key factors influencing changes in CO2 emissions, such as economic activity, industrial structure, energy intensity, energy structure, and emissions coefficient during the 2010–2018 period. The findings indicate that changes in CO2 emissions in industrial sub-sectors vary. High technology firms had the lowest average emissions compared to firms with other technology. Large-sized firms had the lowest emissions than small and medium firms. Foreign private firms had lower emissions than national private firms did. Firms in the Java–Bali location had, on average, highest emissions than those outside Java–Bali. Exporting firms had lower average emissions intensity compared to non-exporting firms. This study’s novelty is an analysis of the effect of components that affect changes in CO2 emissions in firm groups based on their characteristics so that policymakers can focus on the potential reduction in CO2 emissions in certain groups of firms, namely firms that use the most energy intensively, is inefficient, and uses low-quality energy. Comparative analysis using firm characteristics reveals that energy-intensive firms’ economic growth determines changes in CO2 emissions in Indonesia’s manufacturing industry.

China, known as the largest carbon emitter and the second largest economy worldwide, has continued to put effort into the understandings of the main drivers of carbon emission and their decoupling statuses from its economic growth. Considering the significant differences of natural and social environments in different regions of China, this paper presents a regional-scale decomposition of energy-related carbon emission and its decoupling from economic growth by using the Logarithmic Mean Divisia Index (LMDI) and the Tapio decoupling method. The decoupling results indicate that carbon emissions in all regions show a stable decoupling trend from their economic development, which means that China is now on the right road for achieving a low-carbon economy. However, the decoupling status by the end of 2016 also indicates that most of the regions are still in the states of expansive coupling or weak decoupling, especially in Northwest (NW), which implies that the speed of decarbonization process is still not high enough. The decomposition results show that in all regions except NW, GDP per capita is the most influential factor leading to increasing carbon emissions, while energy intensity is the largest factor in reducing carbon emissions. In NW, both GDP per capita and energy intensity drive the increase in carbon emissions. The results in this paper could benefit China's regional policy-making and national strategies.

Amid global climate warming, it is necessary to explore the decoupling relationship between economic growth (EG) and industrial CO2 emissions (ICEs). This paper uses the Tapio decoupling model and the logarithmic mean Divisia index (LMDI) model synthetically to analyze the decoupling relationship between EG and ICEs and reveal the driving force of growth of CO2 emissions (CEs) based on ICE panel data from 13 prefecture-level cities in Jiangsu province from 2011 to 2015. From the research results, it can be seen that the decoupling status in southern Jiangsu, middle Jiangsu and northern Jiangsu presented weak decoupling (WD), weak negative decoupling (WND) and WD, respectively. In 2011–2013, seven prefecture-level cities exhibited states of WD, and strong decoupling (SD) occurred only in Zhenjiang, with a decoupling index value of −0.3359. In 2013–2015, five prefecture-level cities exhibited states of WD. The energy intensity and carbon emission intensity had negative inhibitory effects on ICEs, and economic development and the energy structure had positive promotion effects on ICEs. The research results have important theoretical and practical significance for future energy savings, carbon emissions reductions and the realization of a low-carbon economy in the economic development of Jiangsu.

In order to explore the decoupling relationship and its influence factors between economic growth and carbon emissions in China, the decoupling elasticity decomposition quantitative model of carbon emissions based on extended Log-Mean Divisia Index and Tapio decoupling models is established in this paper. The carbon emissions induced by household (HOU) sector ranked fifth among the nine sectors; hence, we analyzed the HOU sector with the production (PRO) sector together. The results show that, first, the carbon emissions increased from 3.95 billion tons in 1996 to 10.49 billion tons in 2012, and the contributions of manufacturing sector and electric power, gas and water production and supply sector to carbon emissions account for approximately 81 %. Second, the economic output effect is the main contributor, and the energy intensity effect is the major inhibitor factor to the carbon emissions in both the PRO sector and HOU sector, respectively. Third, the decoupling state of the PRO sector mainly stayed at weak decoupling, while the decoupling state of HOU sector mainly stayed at strong decoupling. Fourth, the impact factors of the carbon emissions and the decoupling elasticity values are in complete agreement. At the end of this paper, we present some policy recommendations for China’s government to realize the decoupling between CO2 emissions and economic growth in the near future.