Economic environmental imbalance in China — Inter-city air pollutant emission linkage in Beijing–Tianjin–Hebei (BTH) urban agglomeration

Sun, Yun; Wang, Yuan; Zhang, Zengkai

doi:10.1016/j.jenvman.2022.114601

Cited by 21 publications

(9 citation statements)

References 24 publications

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“…The IO model designed by Leontief can quantify the inflow and outflow relationship between sectors by counting the monetary flows of economic sectors (Leontief, 1936). It can reveal the potential environmental impacts caused by production activities such as resource exploitation, so it was widely applied in the accounting of carbon emissions (Fan et al, 2018;Zha et al, 2022), air pollution (Dong J et al, 2022;Sun et al, 2022), fossil energy (Li et al, 2020;Wang Q et al, 2022), water utilization (Zheng et al, 2020;, and other environmental studies (Zhang et al, 2018;Shi et al, 2021;Jiang and Hao, 2022) . Production-based, consumption-based and incomebased carbon emissions can all be calculated through the SRIO model.…”

Section: Three-perspective Carbon Emission Accounting Methodsmentioning

confidence: 99%

“…Modern cities and industries have significantly improved people's living conditions, but they have also brought enormous pressure on the ecological environment (Giannakis et al, 2019;Sun et al, 2022). Climate disasters caused by greenhouse gas emissions, especially carbon dioxide, have become a serious problem that threatens the survival of all human beings.…”

Section: Introductionmentioning

confidence: 99%

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A characteristics analysis of carbon emission based on multi-dimensional carbon emission accounting methods and structural decomposition analysis: A case study of Beijing, China

Chen²

2023

Front. Earth Sci.

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The city’s industrial transformation leads to a large amount of carbon emissions, which poses a thorny problem for the allocation of carbon responsibilities. This study established a multi-dimension long-term carbon emission analysis model to explore the characteristic of Beijing’s embodied carbon emissions, which could calculate the production-based, consumption-based and income-based carbon emissions. Then, structural decomposition analysis was adopted to quantify the contribution of socioeconomic factors in local and imported carbon emissions. In addition, emission linkage analysis was used for revealing the long-term evolutionary trajectories of sectors. The key discovery can be summarized as follows: 1) the fluctuation trend of production-side and income-side carbon emissions in Beijing is stable and decreased by 3.53% from 2002 to 2017, while consumption-side carbon emissions increased rapidly by 795.45%. 2) The energy, transportation and other services sectors from the supply, production and consumption perspectives. 3)Per capita consumption, production structure and consumption structure are the major contributors of carbon emissions. The study is expected to provide decision support for policymakers to reasonably formulate carbon mitigation policies and allocate carbon mitigation responsibilities from multiple perspectives, and promote the realization of the “carbon peak and carbon neutrality” strategy.

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Section: Three-perspective Carbon Emission Accounting Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A characteristics analysis of carbon emission based on multi-dimensional carbon emission accounting methods and structural decomposition analysis: A case study of Beijing, China

Chen²

2023

Front. Earth Sci.

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“…As the by-products of economic production, the emissions of air pollutants are closely related to the production activities of various economic sectors [ 8 , 9 ]. On the one hand, agriculture, industry and other economic sectors directly discharge air pollutants in the production process [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…Key sectors can be identified according to the direct or embodied pollutant emissions of each sector. Scholars generally believe that the sectors with a large proportion of direct emissions are the key sectors on the production side, while the sectors with a large proportion of embodied emissions is the key sectors on the consumption side [ 9 , 24 ]. In addition, the hypothetical extraction method (HEM) on the basis of the IOA model is often used to analyze the effect of sector separation on total output and further to decompose sectoral linkages [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Characteristics Analysis and Identification of Key Sectors of Air Pollutant Emissions in China from the Perspective of Complex Metabolic Network

Song

Jiang

et al. 2022

IJERPH

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Presently, China is in a critical period of economic transformation and upgrading. At the same time, it is also facing the pressure of serious atmospheric environmental pollution, which seriously threatens human health and hinders the sustainable economic development. Air pollutants are closely related to economic sectors, which together constitute a complex network. Air pollutants form an input–output ecological metabolic relationship among different sectors. Therefore, from the perspective of complex metabolic network, this study first constructs an environmental input–output model and then comprehensively uses the relevant methods of ecological network analysis and complex network analysis to analyze the characteristics of China’s air pollutant emission system. Secondly, the key joint sectors of NOx and PM emissions are determined from the supply side and the demand side, respectively. Finally, the corresponding emission reduction measures are proposed for the identified key sectors.

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“…The studies that form the existing literature on air pollution have examined many different geographical areas, including YRD, the Pearl River Delta (PRD), the Beijing-Tianjin-Hebei region (BTH), individual cities, and the entire country (Zheng et al, 2009;Gong et al, 2021;Zhou et al, 2021;Deng et al, 2022;Sun et al, 2022). These studies have focused on temporal and spatial distributions (Zheng et al, 2009;Deng et al, 2022;Su et al, 2022;Zhang and Cheng, 2022), composition and source apportionment (Xue et al, 2019;Zhou et al, 2021;Xu et al, 2022), influencing factors and health effects (Geng et al, 2021;Zhao et al, 2022), and air quality and transport models (Sulaymon et al, 2021;Li et al, 2022;Qin et al, 2022), so as to reveal the distribution features, composition forms, transport characteristics, and health risks of air pollutants.…”

Section: Introductionmentioning

confidence: 99%

Break point identification and spatiotemporal dynamic evolution of air pollutants: An empirical study from Anhui province, east China

Wang

Gui²,

Wang³

et al. 2022

Front. Environ. Sci.

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The new modality of inter-regional joint prevention and control is increasingly important to the integrated process of collaborative governance of air pollutants. Therefore, it has become necessary to analyze the degree of interaction among air pollutants within and between cities, master the dynamics of their spatiotemporal distribution and its influencing factors, and diagnose the primary obstacle factors. Long-term data on the concentrations of six air pollutants among 16 cities of Anhui province from 2015 to 2020 were analyzed using harmonic regression, the coupling coordination degree model, the obstacle degree model, the logarithmic mean Divisia index (LMDI), and exploratory spatial data analysis (ESDA). Over all, the annual mean concentrations of five of these pollutants (NO2, SO2, CO, PM10, and PM2.5) decreased to a certain extent over time, whereas O3 concentrations increased. The biggest decrease was observed in BZ city, where SO2 decreased by 80.60% (halving time: −2.03 ± 0.02 years), and the biggest increase was observed in CZ city, where O3 increased by 113.85% (doubling time: 1.74 ± 0.01 years). The O3 concentrations in most cities reached their break points starting in 2018, but the break points of other air pollutants appeared earlier than that of O3, mostly before 2018. With the exception of NO2 and O3, the halving times of other air pollutants were basically shorter than the doubling times. The high degree of interaction among air pollutants within and between cities contrasted sharply with the low degree of coordination. An analysis of hotspot evolution revealed that particulate matter (PM10 and PM2.5) migrated to northern Anhui, NO2 and O3 agglomerated to central Anhui, and CO eventually gathered in the Wanjiang City Belt. The primary obstacle factors of air pollutants in Anhui were particulate matter, SO2 and NO2. The seasonal differences in primary obstacle factors were most evident in 2020: NO2 dominated in winter (in 10 cities), SO2 dominated in southern Anhui, and particulate matter dominated in northern and central Anhui in spring. Other seasons were almost entirely dominated by particulate matter. Industrial structure was found to be more effective in reducing industrial carbon emissions, and technological improvement was found to be more advantageous in reducing industrial particulate matter, NOx and SO2. Finally, the policy implications of these results and suggestions for strengthening the inter-city joint prevention and control of air pollutants are discussed.

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Economic environmental imbalance in China — Inter-city air pollutant emission linkage in Beijing–Tianjin–Hebei (BTH) urban agglomeration

Cited by 21 publications

References 24 publications

A characteristics analysis of carbon emission based on multi-dimensional carbon emission accounting methods and structural decomposition analysis: A case study of Beijing, China

A characteristics analysis of carbon emission based on multi-dimensional carbon emission accounting methods and structural decomposition analysis: A case study of Beijing, China

Characteristics Analysis and Identification of Key Sectors of Air Pollutant Emissions in China from the Perspective of Complex Metabolic Network

Break point identification and spatiotemporal dynamic evolution of air pollutants: An empirical study from Anhui province, east China

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