A policy support framework for the balanced development of economy-society-water in the Beijing-Tianjin-Hebei urban agglomeration

Hao, Lingang; Yu, Jingjie; Du, Chunyu; Wang, Ping

doi:10.1016/j.jclepro.2022.134009

Cited by 10 publications

(2 citation statements)

References 55 publications

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“…It has tremendous development advantages in terms of natural resources, distribution, production, markets, talents, and location, making it one of the fastest-growing economic regions in China. In recent years, Beijing and Tianjin, as the central growth points of the region, have stimulated the integrated development of sub-cities in the region, enabling the BTH to achieve a regional GDP of RMB 9.6 trillion in 2021 [36,37]. However, the siphoning effect of large cities has led to unbalanced development within the BTH, exhibiting polarization; rapid urbanization and industrialization have led to ecological degradation, reductions in arable land area, traffic congestion, and deterioration of the population's living environment, which affects sustainable economic and social development [38].…”

Section: Study Areamentioning

confidence: 99%

Production–Living–Ecological Spatial Function Identification and Pattern Analysis Based on Multi-Source Geographic Data and Machine Learning

Bu,

Fu,

Jiang

et al. 2023

Land

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Land use cannot be simply understood as land cover. The same land may carry different functions, such as production, living, and ecological applications; the dominant function of land will affect and restrict other uses. Disorderly urbanization and industrialization have led to an intensification of conflicts among the production, living, and ecological functions of land, which is a major constraint on regional sustainable development. This paper took the perspective of land-use function and used multi-source data such as Sentinel remote-sensing imagery, VIIRS night-time light data, and POIs to classify land-use functions on a large scale in the Beijing–Tianjin–Hebei (BTH) urban agglomeration. The specific research process was as follows. Firstly, the BTH region was multi-scale-segmented based on Sentinel remote-sensing data. Then, the spectral, texture, shape, and socio-economic features of each small area after segmentation were extracted. Moreover, a PLES land-use classification system oriented towards land-use function was established, and a series of representative samples were selected. Subsequently, a random forest model was trained using these samples; then, the trained model was used for the large-scale analysis of land use in the entire BTH region. Finally, the spatial distribution patterns and temporal–spatial evolution characteristics of PLES in the BTH region from 2016 to 2021 were analyzed from the macro level to the micro level.

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Section: Study Areamentioning

confidence: 99%

Production–Living–Ecological Spatial Function Identification and Pattern Analysis Based on Multi-Source Geographic Data and Machine Learning

Bu,

Fu,

Jiang

et al. 2023

Land

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“…It is one of the regions with the most active economic development in China and one of the most important political, economic, and cultural centers in China. The unique geographical location provides important infrastructure and convenience for logistics development [3]. In 2020, the GDP of the Beijing-Tianjin-Hebei region will be 8639.3 billion yuan, an increase of 2.4% over the previous year, accounting for 8.5% of the national total; the added value of the logistics industry will account for 7.8% of GDP, an increase of 0.2 percentage points over the previous year; the express business volume and express business revenue will increase by 60.7% and 38.2%, respectively.…”

Section: Introductionmentioning

confidence: 99%

Green and Low-Carbon Efficiency Assessment of Urban Agglomeration Logistics Industry: Evidence from China’s Beijing-Tianjin-Hebei Metropolitan Area (2008–2020)

Wang,

Tian

2023

Sustainability

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With the advent of the post-industrial era, the rapid development of e-commerce has propelled the logistics industry to become the lifeline of the national economy, supporting the orderly flow of resource elements between cities. However, the concerning issues of excessive energy consumption and low logistics efficiency in the transportation process have come to the forefront. The introduction of China’s dual-carbon policy goals indicates that enhancing regional logistics’ green and low-carbon efficiency is key to solving the global logistic sustainability problem. Nowadays, the logistics sector’s efficiency in producing green and low-carbon emissions has been quantified using an input-output measurement index. Using data from 2008 to 2020 from the dynamic panel of the logistics sector in the urban agglomerations of Beijing, Tianjin, and Hebei, the three-stage SBM-DEA and Malmquist index quantitative evaluation models are selected to estimate the logistic green and low-carbon development efficiency comprehensively. The analysis discovered that green and low-carbon logistics in the Beijing-Tianjin-Hebei metropolitan agglomeration are relatively efficient overall, and the urban siphon effect of Beijing and Tianjin is noticeable. Once the impact of environmental variables and random errors is eliminated, it becomes evident that these factors tend to inflate the overall technical efficiency. Technical efficiency levels are the primary factor leading to regional logistics inefficiencies. Additionally, it is essential to note that scale efficiency positively affects urban development, leading to a rebound effect, summarizing the existing problems combined with the visualization map, and putting forward corresponding policy suggestions, which is of great practical significance.

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County-Level Synergy Analysis Between Air Pollution and CO2 Emissions

Wang,

2024

Aerosol Sci Eng

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A policy support framework for the balanced development of economy-society-water in the Beijing-Tianjin-Hebei urban agglomeration

Cited by 10 publications

References 55 publications

Production–Living–Ecological Spatial Function Identification and Pattern Analysis Based on Multi-Source Geographic Data and Machine Learning

Production–Living–Ecological Spatial Function Identification and Pattern Analysis Based on Multi-Source Geographic Data and Machine Learning

Green and Low-Carbon Efficiency Assessment of Urban Agglomeration Logistics Industry: Evidence from China’s Beijing-Tianjin-Hebei Metropolitan Area (2008–2020)

County-Level Synergy Analysis Between Air Pollution and CO2 Emissions

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