Health benefits from the reduction of PM2.5 concentrations under carbon tax and emission trading scheme: a case study in China

Chen, Shuyang; Wang, Can

doi:10.1007/s11356-022-24781-0

Cited by 8 publications

(1 citation statement)

References 58 publications

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“…They found that the policies primarily aimed at reducing CO 2 emissions, such as the low-carbon city pilot policy and emissions trading system pilot programs, played important roles in alleviating PM 2.5 pollution [23,24]. In addition to reducing PM 2.5 emissions, Chen and Wang also found that a long-term emission trading scheme policy could decrease PM 2.5 -associated morbidity and mortality [25]. Some studies quantitatively assessed the impact of CO 2 emissions on PM 2.5 emissions.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Synergy between CO2 and PM2.5 Emissions Reduction: A Case Study of China’s 329 Cities

Wang,

Zhang,

Cheng

2023

Atmosphere

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The synergetic reduction of CO2 and PM2.5 emissions has received much attention in China in recent years. A comprehensive evaluation of the synergy between CO2 emission reduction (CER) and PM2.5 emission reduction (PER) would provide valuable information for developing synergetic control policies. Thus, we constructed a comprehensive CO2-PM2.5-emission-reduction index system and evaluated the synergy between CER and PER, using the coupling coordination degree (CCD) and relative development degree (RDD) model in China’s 329 cities from 2003 to 2017. The spatiotemporal characteristics of the CCD were analyzed on the national, regional, and urban scales. Furthermore, we used the spatial autocorrelation analysis, kernel density estimation, and Dagum Gini coefficient to investigate the spatial autocorrelation, evolutionary characteristics, and regional differences of the CCD. The results indicate that (1) the synergy between CO2 and PM2.5 emissions’ reductions showed an upward trend, and the lowest CCD values occurred in NW and Shanghai on the regional and urban scales, respectively; (2) the CCD showed obvious spatial clustering characteristics, with 75% of the cities located in the “High–High” or “Low–Low” clustering zones in the Moran scatter plots in 2017; (3) the polarization of CCD in SC, MYR, and SW showed intensified trends; (4) and the hypervariable density was the largest contributor to the overall difference in the CCD. Our findings suggest that more attention should be paid to the top-level design of the policies, technological innovation, and cross-regional or intercity cooperation.

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Section: Introductionmentioning

confidence: 99%