Mutual effects of CO2 emission reduction and air pollution control policies in beijing-tianjin-hebei region

Wu, Kai; Liu, Xiaorui; Dai, Hancheng; Zhang, Silu; Zhou, Ziqiao; Ma, Teng; Guo, Chaoyi; He, Xiaohui; Fang, Yan

doi:10.3389/fenvs.2022.1006142

Cited by 10 publications

(5 citation statements)

References 37 publications

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“…The prolonged suspension of fine particles in the atmosphere facilitates the formation of PM 2.5 pollution. Research indicates that industrial emissions, along with coal and biomass combustion for daily living, are principal sources of PM 2.5 [14][15][16]. Additionally, socio-economic and meteorological factors, as well as the generation of secondary particulate matter, further contribute to PM 2.5 pollution [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Effects of Big Data on PM2.5: A Study Based on Double Machine Learning

Wei,

Cheng,

Duan

et al. 2024

Land

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The critical role of high-quality urban development and scientific land use in leveraging big data for air quality enhancement is paramount. The application of machine learning for causal inferences in research related to big data development and air pollution presents considerable potential. This study employs a double machine learning model to explore the impact of big data development on the PM2.5 concentration in 277 prefecture-level cities across China. This analysis is grounded in the quasi-natural experiment named the National Big Data Comprehensive Pilot Zone. The findings reveal a significant inverse relationship between big data development and PM2.5 levels, with a correlation coefficient of −0.0149, a result consistently supported by various robustness checks. Further mechanism analyses elucidate that big data development markedly diminishes PM2.5 levels through the avenues of enhanced urban development and land use planning. The examination of heterogeneity underscores big data’s suppressive effect on PM2.5 levels across central, eastern, and western regions, as well as in both resource-dependent and non-resource-dependent cities, albeit with varying degrees of significance. This study offers policy recommendations for the formulation and execution of big data policies, emphasizing the importance of acknowledging local variances and the structural nuances of urban economies.

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Section: Introductionmentioning

confidence: 99%

Effects of Big Data on PM2.5: A Study Based on Double Machine Learning

Wei,

Cheng,

Duan

et al. 2024

Land

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“…Therefore, China is now faced with dual challenges, including air pollution control and carbon mitigation. The uniqueness of today's atmospheric environment urges the Chinese government to explore a well-developed policy or governance strategy with aim of achieving a "win-win" situation of air pollutants and CO2 emissions control (Wu et al, 2022). Identifying the mechanism behind various factors influencing the performance of synergistic governance is the key point to provide valuable suggestions for the further realization of carbon neutrality and air quality improvement in China.…”

Section: Introductionmentioning

confidence: 99%

“…According to the existing literature regarding the evaluation on the co-benefit and co-control performance between air pollution and carbon mitigation, the synergistic effect was analysed from the perspective of comprehensive consideration of the respective relationship between carbon dioxide and air pollutant emission, or building one indicator or parameter to represent the degree of co-benefit or co-control (Dong et al, 2019a;Wu et al, 2022;Yi et al, 2022). In this paper, an index system approach is performed to measure the synergy degree, defined as the coupling coordination degree between two subsystems.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Spatiotemporal Heterogeneity of Drivers on Province-Level Synergy of Air Pollution Control and Carbon Mitigation in China

Guo,

Chen,

Hamm

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Confronting the dual challenges of air pollution control and carbon mitigation, China is seeking a "win-win" pathway to achieve the co-reduction of air pollution and CO2 emission. In this study, taking 31 provinces in China as research objects, a novel index system, characterized by total emission, emission intensity and emission per capita, was established to evaluate the coupling coordination degree between the air pollution control (AP) subsystem and carbon mitigation (CM) subsystem at the provincial scale from 2013 to 2017. Local Geary’s C Index was performed to determine where the coupling coordination was correlated to its neighbour provinces. Considering the discrepancy of the socio-economic status, industrial and energy structure among provinces, Geographically and Temporally Weighted Regression (GTWR) was employed to measure the spatiotemporal heterogeneity of the influences of the driving factors on the coupling coordination degree, aiming to reduce uncertainty in the interpretation of spatial and temporal variability. Results show that: (1) Since the implementation of "Clean Air Action" in 2013, there has been an obvious improving trend of synergistic governance of air pollution control and carbon mitigation. (2) The synergy of air pollution control and carbon mitigation did not achieve good joint governance, locally. (3) The influence of drivers presents significant spatiotemporal heterogeneity among provinces. Generally, the improvement of the coupling coordination is greatly affected by the local energy structure and economic level. This conclusion has propounded practical significance for synergistic governance of air quality improvement and low-carbon development in the context of carbon neutrality achievement.

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“…Meanwhile, many studies have quantified the air quality co-benefits of climate policies [2,[16][17][18][19][20]. These studies have shown that these co-benefits can partially cover or even offset the cost of mitigation [2,18], while there is significant regional and sectoral heterogeneity [2,18].…”

Section: Introductionmentioning

confidence: 99%

Uncovering the key mechanisms of how deep decarbonization benefits air pollution alleviation in China

Liu

Guo²,

Ma³

et al. 2022

Environ. Res. Lett.

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Addressing climate change and air pollution goals in conjunction would be efficient and cost-effective. Dealing with these two challenges is a common issue for urban clusters pursuing sustainable development. Expected to become the fourth international first-class bay area, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) takes the lead in exploring a green and low-carbon transition path as a key element of being a pioneering economic reform demonstration zone. Based on an integrated modeling framework that couples an energy-economy model (IMED|CGE), decomposition analysis, and regression methods, the potential contribution of achieving the climate mitigation target to air pollutant reduction in the GBA by 2050 was quantified. The results showed that the transport sector has the most significant potential for carbon reduction. Energy intensity and structural transformations are the main contributors to reducing carbon emissions, with the latter becoming increasingly important over time. Climate policy can effectively reduce air pollutant emissions; however, this effect varies for different pollutants and sectors. Based on the assessment of the synergy index and cost of abatement, sectors with priority for synergic governance were identified. The regression results indicated that the carbon shadow price would be significantly more effective in reducing air pollutant emissions in the post-2030 period than before 2030, except for SO2 and NH3, partially because of the existing actions that cause the synergistic effects to decline. In addition, end-of-pipe removal measures still play a relatively significant role in reducing air pollutants, particularly VOC, NH3, and primary PM2.5. Thus, the findings suggest that priority should be given to sectors with huge synergistic benefits, such as transportation and power generation while paying attention to possible trade-offs.

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Mutual effects of CO2 emission reduction and air pollution control policies in beijing-tianjin-hebei region

Cited by 10 publications

References 37 publications

Effects of Big Data on PM2.5: A Study Based on Double Machine Learning

Effects of Big Data on PM2.5: A Study Based on Double Machine Learning

Analysis of the Spatiotemporal Heterogeneity of Drivers on Province-Level Synergy of Air Pollution Control and Carbon Mitigation in China

Uncovering the key mechanisms of how deep decarbonization benefits air pollution alleviation in China

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