Identifying carbon emission characteristics and carbon peak in China based on the perspective of regional clusters

Chen, Shuai; Yao, Shunbo; Cheng, Xue

doi:10.1007/s11356-022-24020-6

Cited by 8 publications

(2 citation statements)

References 68 publications

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“…The k-means clustering algorithm is designed to divide complex research objects into classes, with no categories being determined beforehand. In terms of classification characteristics at the national city scale, the k-means clustering algorithm is more appropriate, as shown in [40]. At the same time, scholars have modeled and calculated projections of peak carbon emissions in an attempt to help policymakers develop reasonable emission reduction strategies.…”

Section: Research On Carbon Peak Predictionmentioning

confidence: 99%

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Can China Achieve Its Carbon Emission Peak Target? Empirical Evidence from City-Scale Driving Factors and Emission Reduction Strategies

Zhang,

Wang,

Yang

et al. 2023

Land

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The development of differentiated emission reduction strategies plays an important role in achieving carbon compliance targets. Each city should adopt carbon reduction strategies according to its carbon emission characteristics. China is a vast country, and there are significant differences between cities. Therefore, this study classifies 340 Chinese cities according to their carbon emission characteristics since 2020 and proposes differentiated emission reduction strategies accordingly. The results of the research show that Chinese cities can be divided into four categories, and they can strive to achieve their carbon peak targets by adopting differentiated emission reduction strategies. In the baseline scenario, Chinese cities will not be able to meet the peak carbon target by 2030. In the differentiated scenario, eco-agricultural cities, industry-led cities, and high-resource-availability cities will be able to achieve peak carbon by 2030. Unfortunately, resource-poor cities will not reach their peak. However, the extent to which their total carbon emissions contribute to the achievement of national goals is low, and their carbon emissions can be traded off for economic development by appropriately relaxing the constraints on carbon emissions. Therefore, in order to achieve China’s peak carbon goal, this study proposes emission reduction recommendations that should be adopted by different types of cities to form differentiated emission reduction strategies.

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Section: Research On Carbon Peak Predictionmentioning

confidence: 99%

“…The model variables are shown in Table 1. In this study, population (P) was treated as a proxy for the number of permanent residents [40]. The indications are that the larger a population is, the higher the carbon emissions are.…”

Section: Stirpat Modelmentioning

confidence: 99%