A comparative study on decoupling relationship and influence factors between China's regional economic development and industrial energy–related carbon emissions

Zhou, Xing; Zhang, Ming; Zhou, Meihua; Zhou, Min

doi:10.1016/j.jclepro.2016.09.115

Cited by 192 publications

(76 citation statements)

References 81 publications

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“…The concept of "decoupling" also originated from physics, which means dissociating the relationships among some physical variables [50][51][52][53][54][55][56][57]73]. Tapio (2005) [54] defined decoupling index based on the concept of elasticity analysis and provided eight logical possibilities for decoupling states, namely weak decoupling, expansive coupling, expansive negative decoupling, strong negative decoupling, weak negative decoupling, recessive coupling, recessive decoupling and strong decoupling.…”

Section: Temporal Decoupling Classification Methodsmentioning

confidence: 99%

Exploring the Coupling and Decoupling Relationships between Urbanization Quality and Water Resources Constraint Intensity: Spatiotemporal Analysis for Northwest China

Bao

Zou

2017

Sustainability

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Abstract:China is faced with great challenges for its low urbanization quality and high water stress. Moreover, the relationship between urbanization quality and water resources is still ambiguous. Therefore, we firstly constructed an urbanization quality index (UQI) and a water resources constraint intensity index (WRCI) by a fuzzy comprehensive evaluation method with multi-objectives and multi-hierarchies. Secondly, based on the concept and method of "coupling" and "decoupling", we provided a method to explore the coordinated and uncoordinated relationships between UQI and WRCI from a spatiotemporal perspective. Finally, we used the statistical data of 51 prefecture level regions in Northwest China from the period 2000-2014 to analyze the spatiotemporal variation of the coupling and decoupling relationships between UQI and WRCI. Results show that, the UQI and WRCI in the whole Northwest China both belonged to low level, and that they had achieved strong decoupling during 2000-2014. However, the coupling and decoupling relationships between UQI and WRCI in Northwest China had great spatial disparity. From the HL-type regions (regions with high UQI & low WRCI) and strong decoupling type regions, we can find key development areas of Northwest China, where the relationships between UQI and WRCI were optimal and coordinated. From the LH-type regions (regions with low UQI & high WRCI) and strong negative decoupling type regions, we can find key problem areas, where the relationships between UQI and WRCI were the worst and uncoordinated. Our study developed an effective method for evaluating the sustainable development level of urbanization constrained by water resources in Northwest China and similar regions, which is significant for the New-Type Urbanization research in China.

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Section: Temporal Decoupling Classification Methodsmentioning

confidence: 99%

Exploring the Coupling and Decoupling Relationships between Urbanization Quality and Water Resources Constraint Intensity: Spatiotemporal Analysis for Northwest China

Bao

Zou

2017

Sustainability

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“…More specifically, it should be noted that the percentage of industrial energy-related carbon emissions has decreased each year in the Central region and in the Beijing-Tianjin region, while the reverse is true in the Northern coastal regions [13]. For other regions, the proportion of industrial energy-related carbon emissions has remained stable.…”

Section: Energy-related Issuesmentioning

confidence: 99%

“…From a regional perspective, Zhou et al [13] find that a faint decoupling nexus between carbon emissions from industrial energy use and economic growth exist in most regions of China during most periods. Chen and Yang [85] state the economic effect has the most powerful positive influence on carbon emissions, which is more forceful in less-developed provinces (especially those featuring heavy industry) than in developed regions (See Figure 6).…”

Section: Economic Developmentmentioning

confidence: 99%

“…Zhou et al [13] confirm that China's carbon mitigation technology lags behind developed countries, its contributions to economic growth and industry energy carbon emission decoupling are limited. Technological progress is decomposed by Liu et al [99] into technology introduction and technological innovations which both have negative effects on carbon emissions and are conducive to improving the quality of atmospheric environment.…”

Section: Foreign Direct Investment and Technologymentioning

confidence: 99%

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Peak Carbon Emissions in China: Status, Key Factors and Countermeasures—A Literature Review

Dong

Hua

2018

Sustainability

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China is continually increasing its efforts to reduce carbon emissions due to dual pressure domestically and internationally. Specifically, China has committed to achieving peak carbon emissions no later than 2030. As an international goal, carbon emission peak in China has generated considerable research interest. However, most scholars only focus on a partial aspect of peak carbon emissions. Through a review of literature from numerous sources, this paper provides a more systematic analysis than previously available of how China can reach its peak carbon emissions as early as possible. This study first elaborates the status of peak carbon emissions in China from regional and sectoral perspectives and summarizes the various predictions. Then, five main driving factors of carbon emission peak in China are investigated, i.e., economic development, urbanization, energy-related issues, foreign direct investment and technology, and transportation. Corresponding to these five factors, some specific and practical recommendations are proposed. Furthermore, regional differences and spatial "spillovers" must be taken into account, thereby achieving a "win-win" solution with respect to the integration of emission mitigation and economic development, as well as reaching the desired target of peak carbon emissions.

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“…The Kaya formula has become one of the main analytical frameworks for studying the changing characteristics of CO 2 emissions and their influencing factors [8]. Population, energy intensity, affluence and energy carbon intensity are the key determinant factors [9]. On the basis on the Kaya formula, subsequent research on drivers in structure, size, technology and other directions continues to expand and enrich.…”

Section: Introductionmentioning

confidence: 99%

Spatial Heterogeneity of Energy-Related CO2 Emission Growth Rates around the World and Their Determinants during 1990–2014

et al. 2017

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Understanding the spatial heterogeneity and driving force identification of energy-related CO 2 emissions (ECEs) can help build consensus for mitigating CO 2 emissions and designing appropriate policies. However, previous studies on ECEs that focus on both the global-regional scale and the interaction of factors have been seldom conducted. In this paper, ECE data from 143 countries from 1990 to 2014 were selected to analyze regional differences in ECE growth rates by using the coefficient of variation. Then a geographical detector was used to analyze the key determinant factors on ECE growth rates around the world and in eight types of regions. The results show that: (1) the ECE growth rate in the Organization for Economic Cooperation and Development (OECD) region is low and tended to decrease, while in the non-OECD region it is high and tended to increase; (2) the coefficient of variation and detection factor of ECE growth rates at a regional scale are higher than those at a global scale; (3) in terms of the key determinant factors, population growth rate, growth rate of per capita GDP, and energy intensity growth rate are the three key determinant factors of ECE growth rates in the OECD region and most of the non-OECD regions such as non-OECD European and Eurasian (NO-EE), Asia (NO-AS), non-OECD Americas (NO-AM). The key determinant factors in the African (NO-AF) region are population growth rates and natural gas carbon intensity growth rates. The key determinant factors of the Middle East (NO-ME) are population growth rate, coal carbon intensity growth rate and per capita GDP growth rate; (4) the determinant power of the detection factor, the population growth rate at the global scale and regional scale is the strongest, showing a significant spatial consistency. The determinant power of per capita GDP growth rate and energy intensity growth rate in the OECD region, respectively, rank second and third, also showing a spatial consistency. However, the carbon intensity growth rates of the three fossil fuels contribute little to the growth rate of ECEs, and their spatial coherence is weak; (5) from the perspective of the interaction of detection factors, six detection factors showed bilinear or non-linear enhancement at a global and a regional scale, and the determinant power of the interaction of factors was significantly enhanced; and (6) from the perspective of ecological detection, the growth rate of carbon intensity and the growth rate of natural gas carbon intensity at the global scale and NO-ME region are significantly stronger than other factors, with a significant difference in the spatial distribution of its incidence. Therefore, the OECD region should continue to reduce the growth of energy intensity, and develop alternative energy resources in the future, while those that are plagued by carbon emissions in non-OECD regions should pay more attention to the positive influence of lower population growth rates on reducing the growth rate of energy-related CO 2 emissions. Reducing energy intensi...

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A comparative study on decoupling relationship and influence factors between China's regional economic development and industrial energy–related carbon emissions

Cited by 192 publications

References 81 publications

Exploring the Coupling and Decoupling Relationships between Urbanization Quality and Water Resources Constraint Intensity: Spatiotemporal Analysis for Northwest China

Exploring the Coupling and Decoupling Relationships between Urbanization Quality and Water Resources Constraint Intensity: Spatiotemporal Analysis for Northwest China

Peak Carbon Emissions in China: Status, Key Factors and Countermeasures—A Literature Review

Spatial Heterogeneity of Energy-Related CO2 Emission Growth Rates around the World and Their Determinants during 1990–2014

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