Study of county-level low-carbon standards in China based on carbon emissions per capita

Hou, Li‐Peng; Tang, Lina; Yongfu, Qian; Chen, H.; Wang, L.

doi:10.1080/13504509.2019.1672589

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…In order to leave enough space for economic development, PCCE should be set to a fair number. Too low’s PCCE will limit the scale of economic development; too high’s PCCE will put high pressure on achieving the emission reduction target (Hou et al 2019 ). China’s county PCCE dynamic benchmarking system links economic development to PCCE benchmarking by benchmarking the relationship between PCGDP and PCCE in developed countries, making the low-carbon target dynamic and solving the problem of too high or too low PCCE target.…”

Section: Discussionmentioning

confidence: 99%

Study on the extension of the dynamic benchmark system of per capita carbon emissions in China’s county

Yang

Shi

Wang

et al. 2022

Environ Sci Pollut Res

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County is the center of China’s socio-economic development and the key node for urban–rural integration. Also, the county is an important carrier for promoting urban and rural green development. Improving green and low-carbon development capabilities and formulating county-level low-carbon standards will play a significant role in promoting China’s new people-oriented urbanization and rural revitalization. Although there have been extensive studies on low-carbon benchmarks, over half of the benchmarks tend to ignore the development stage of the evaluated region and its needs. When the region’s economy reaches a certain level, constraints from low-carbon targets may limit the local development process. This study firstly allocated county carbon intensity reduction targets (CIRT) by considering the differences in county carbon reduction capacity and responsibility. Secondly, a dynamic benchmark system of per capita carbon emissions (PCCE) in counties in China is constructed. Finally, we took Changxing County in Zhejiang Province as a research case to verify the dynamic benchmark of PCCE. According to the carbon intensity target reduction rate (CITRR), China’s counties can be divided into three categories: low carbon emissions reduction capability-responsible counties (L-CERCRC), medium carbon emissions reduction capability-responsible counties (M-CERCRC), and high carbon emissions reduction capability-responsible counties (H-CERCRC). The results show that (1) due to the national CO 2 emission reduction target in 2030, the carbon intensity will be 60% lower than in 2005, the CITRR for China’s 1510 counties range from 8.36 to 137.83%; the average CITRR is 48.40%. (2) Changxing County’s CITRR is 57.71%, which belongs to the H-CERCRC. The PCCE of Changxing County will be much higher than the benchmark when the carbon peak is reached in the future. (3) For reaching the aiming benchmark, Changxing County is suggested to adjust its relevant influencing factors of PCCE for converting local’s PCCE reaching to the benchmark within a certain time period. The dynamic benchmark system for PCCE in China’s counties established in this study is economically sensitive, which not only takes the differences of counties into account, but also meets the needs of counties’ diverse development form stages. This system provides counties a few coordinated directions which can improve the local’s economic development and reduce greenhouse gas (GHGs) emissions through the development progress.

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Section: Discussionmentioning

confidence: 99%

Study on the extension of the dynamic benchmark system of per capita carbon emissions in China’s county

Yang

Shi

Wang

et al. 2022

Environ Sci Pollut Res

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“…This paper explores the effect of the development level of the DE on urban CE. At present, there are two main choices for explanatory variables in the research on CE reduction: one is per capita CE [ 49 , 50 ] and the other is total emissions [ 51 , 52 ]. This paper selected the total urban emissions as the explained variable in this study.…”

Section: Methodsmentioning

confidence: 99%

Digital Economy and Environmental Quality: Insights from the Spatial Durbin Model

Wang

Sun

Zhang

et al. 2022

IJERPH

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Recent developments in attaining carbon peaks and achieving carbon neutrality have had enormous effects on the world economy. Digitalization has been considered a viable way to curtail carbon emissions (CE) and promote sustainable economic development, but scant empirical studies investigate the link between digitalization and CE. In this context, this study constructs the digitalization index using the entropy value method and spatial Markov chain, and the spatial Durbin model is employed to analyze its impact mechanism and influence on urban CE in 265 prefecture-level cities and municipalities in China from 2011 to 2017. The results indicate that: (1) The overall development level of the digital economy (DE) posed a significant spatial effect on urban environmental pollution. However, the effect varies according to the different neighborhood backgrounds. (2) The DE impedes urban environmental deterioration directly and indirectly through the channels of industrial structure, inclusive finance, and urbanization. (3) The development of the DE significantly reduces pollution in cities belonging to urban agglomerations, while the development of the DE escalates emissions in nonurban agglomeration cities. Finally, based on the results, important policy implications are put forward to improve the environmental quality of cities.

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“…The most important principle for the regional decomposition of carbon emission reduction targets is equity [52]. Based on the differences between cities, this study makes appropriate adjustments based on the overall average reduction range, so that the emission reduction tasks undertaken by each city can be matched with its own conditions.…”

Section: Methods Deficienciesmentioning

confidence: 99%

The Allocation of Carbon Intensity Reduction Target by 2030 among Cities in China

2020

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The regional allocation of carbon emission quotas is of great significance to realize the carbon emission target. Basing on the combination of the multi-index method and the improved equal-proportion distribution method, and fully considering the differences in economic factors, population factors, energy factors, technological factors among cities, China’s 2030 carbon intensity reduction target was allocated. The results indicate that: (1) Under the target constraint of 60% reduction in CO2 emissions per unit of Gross Domestic Product (GDP) (carbon intensity) in 2030 compared to 2005, the carbon intensity target reduction rate (CITRR) of 285 Chinese cities is between 17.65% and 141.14%, with an average reduction rate of 51.52%; (2) the CITRR of cities presents significant spatial positive correlation, and the Global Moran I correlation index is 0.38; and (3) the distribution trend of CITRR is the same as the general trend of economic development of China, showing a basic trend of gradual decline from south to north and from coastal to inland. The allocation method takes into account fairness and efficiency, and reflects the differences between cities, so that the allocation results are likely to be accepted by all parties. Meanwhile, this method breaks the limitation of the lack of city’s data and is likely to implement in actual operation. Cities should choose distinguished low-carbon economic development paths, in combination with their characteristics of economic and social development, and carry out inter-city cooperation to promote carbon emission reduction steadily.

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Study of county-level low-carbon standards in China based on carbon emissions per capita

Cited by 6 publications

References 12 publications

Study on the extension of the dynamic benchmark system of per capita carbon emissions in China’s county

Study on the extension of the dynamic benchmark system of per capita carbon emissions in China’s county

Digital Economy and Environmental Quality: Insights from the Spatial Durbin Model

The Allocation of Carbon Intensity Reduction Target by 2030 among Cities in China

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