Illustrating the multi-stakeholder perceptions of environmental pollution based on big data: Lessons from China

Liu, Haimeng; Liu, Huaming; Cheng, Yi

doi:10.1016/j.regsus.2022.03.003

Cited by 8 publications

(3 citation statements)

References 57 publications

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“…Yearly population density data (PD, 1 km) from NASA were also used [42]. In addition to this, the study also obtained data from the China Statistical Yearbook on the number of industrial enterprise units (IEU), GDP, and the number of resident residents (RR) at the county level in Beijing, Tianjin, and Hebei for 2019-2022 to refine the modeling [43]. Table 1 shows the relevant parameters of the spatiotemporal auxiliary variables used in this study.…”

Section: Spatiotemporal Ancillary Datamentioning

confidence: 99%

High-Resolution Daily Spatiotemporal Distribution and Evaluation of Ground-Level Nitrogen Dioxide Concentration in the Beijing–Tianjin–Hebei Region Based on TROPOMI Data

Dai

Wang

et al. 2023

Remote Sensing

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This study utilized TROPOMI remote sensing data, MODIS remote sensing data, ground observation data, and other ancillary data to construct a high-resolution spatiotemporal distribution and evaluation of ground-level NO2 concentrations in the Beijing–Tianjin–Hebei (BTH) region using the Geographic Temporal Neural Network Weighted Regression (GTNNWR) model. Through this model, we obtained the daily distribution of ground-level nitrogen dioxide (NO2) concentrations in the Beijing–Tianjin–Hebei region at a resolution of 500 m for the period of 2019–2022. The research results exhibited higher accuracy and more detailed features compared to other models, enabling a more accurate reflection of the spatial distribution and temporal variations of ground-level NO2 concentrations in the region, while retaining more details and trends and excluding the influence of noisy data. Furthermore, we conducted an evaluation analysis considering important events such as public health incidents and the Winter Olympics. The results demonstrated that the GTNNWR model outperformed the Random Forest (RF), Convolutional Neural Network (CNN), and Geographic Neural Network Weighted Regression (GNNWR) models in performance metrics such as R2, RMSE, MAE, and MAPE, showcasing greater reliability when considering spatiotemporal heterogeneity and spatiotemporal non-stationarity. This study provides crucial data support and reference for atmospheric environmental management and pollution prevention and control in the Beijing–Tianjin–Hebei region.

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Section: Spatiotemporal Ancillary Datamentioning

confidence: 99%

High-Resolution Daily Spatiotemporal Distribution and Evaluation of Ground-Level Nitrogen Dioxide Concentration in the Beijing–Tianjin–Hebei Region Based on TROPOMI Data

Dai

Wang

et al. 2023

Remote Sensing

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“…The government should exert the role of territorial space planning in managing land use and strictly define red lines for ecological protection and urban construction land (Bai et al, 2016). It is also essential to raise citizens' awareness of ecological protection and resource conservation (Liu et al, 2022b).…”

Section: Policy Implicationsmentioning

confidence: 99%

Sustainability assessment of coupled human and natural systems from the perspective of the supply and demand of ecosystem services

et al. 2022

Self Cite

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The sustainability of coupled human and natural systems (CHANS), linked to several of the United Nation’s Sustainable Development Goals, affects both global sustainable development and human wellbeing. However, few studies have explored the relationship between the whole human system and the natural system from the perspective of supply and demand. Based on ecosystem service concept and Maslow’s hierarchy of needs, we constructed an ecosystem services supply index (ESSI) and a human demand index (HMDI) and then analyzed the spatial mismatch of the ESSI and HMDI in 338 prefecture cities in China. We propose a supply-demand balance index inspired by the doughnut theory and demonstrate the conflicts and synergies between ecosystems and human systems. Our results show that the ESSI decreased from 2000 to 2010, but remained almost unchanged from 2010 to 2020. The low ESSI values were mainly distributed over the North China Plain, the Yangtze River Delta, and the Sichuan Basin. The HMDI increased from 2000 to 2020, but the rate of increase has slowed since 2010. The high HMDI values were mainly distributed over the east coast of China, Inner Mongolia, and part of Xinjiang province. From 2000 to 2020, the cities with spatial mismatches of the ESSI and HMDI showed a significant increase and spatial agglomeration. The sustainability of most regions showed a decreasing trend, and the higher the value of the supply-demand balance index, the faster the speed of decrease. Overall, there were more regions in a state of conflict between the natural ecosystem and humans than in a synergistic state, although this number has decreased since 2010. Urban agglomerations and western regions should receive more attention for their ecological health. This study provides a new methodology to assess the sustainability of CHANS that could be applied to other locations. Our findings could support the Chinese government in regional ecosystem governance and territorial space planning.

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“…With the advancement of industrialization and urbanization, China's economy has achieved high growth rates, but accompanying environmental problems have become increasingly serious [1]. Some studies have shown that China's high economic growth is largely at the cost of environmental degradation, and that traditional uneven growth has caused serious ecological and environmental pressure, with resource and environmental problems threatening the lives of residents, as well as the green and sustainable development of the economy [2].…”

Section: Introductionmentioning

confidence: 99%

Impact of Graduate Student Expansion and Innovative Human Capital on Green Total Factor Productivity

Yao

Qian

2023

Sustainability

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Using data from 30 provinces and cities in China from 2005–2018, panel regression models, mediation models, quantile regressions and threshold regressions were used to examine the relationship between graduate student size expansion, innovative human capital and green total factor productivity (GTFP) and analyze the influence mechanisms and heterogeneity among them. The results of the study are as follows: First, graduate student expansion and innovative human capital are the driving force for GTFP growth in China, and graduate student size expansion indirectly boosts GTFP by promoting the supply of innovative human capital, with a 73% mediating effect. Second, the effects of graduate student expansion and innovative human capital on GTFP show a trend of diminishing marginal benefits, and the estimated coefficient of the effect of graduate student scale share on GTFP has an inverted U-shaped relationship. Third, there is a significant threshold feature of industrial structure upgrading in the influence of graduate student scale expansion on GTFP. The study makes suggestions in terms of expanding of the graduate students, rationalizing the enrollment structure of universities, optimizing the regional industrial structure and creating a social innovation environment.

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Illustrating the multi-stakeholder perceptions of environmental pollution based on big data: Lessons from China

Cited by 8 publications

References 57 publications

High-Resolution Daily Spatiotemporal Distribution and Evaluation of Ground-Level Nitrogen Dioxide Concentration in the Beijing–Tianjin–Hebei Region Based on TROPOMI Data

High-Resolution Daily Spatiotemporal Distribution and Evaluation of Ground-Level Nitrogen Dioxide Concentration in the Beijing–Tianjin–Hebei Region Based on TROPOMI Data

Sustainability assessment of coupled human and natural systems from the perspective of the supply and demand of ecosystem services

Impact of Graduate Student Expansion and Innovative Human Capital on Green Total Factor Productivity

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