Spatial Evolution of Urban Expansion in the Beijing–Tianjin–Hebei Coordinated Development Region

Tang, Zhanzhong; Zhang, Zengxiang; Zuo, Lijun; Xiao, Wang; Zhao, Xiaoli; Liu, Fang; Hu, Shunguang; Liu, Yi; Xu, Jilian

doi:10.3390/su13031579

Cited by 9 publications

(4 citation statements)

References 61 publications

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“…In 2015, the Chinese government considered and adopted the Outline of the Beijing-Tianjin-Hebei Cooperative Development Plan, which aims to decongest Beijing's noncapital functions, create a future-oriented capital economic circle, promote innovation in regional development institutions and mechanisms, investigate new models of regional economic development, and meet the needs for coordinated and sustainable development of population, economy, resources, and environment [56][57][58]. In this context, urbanization and industrialization in the Beijing-Tianjin-Hebei region have further continued to advance rapidly, industries have undergone substantial industry transfers, and regional land use structures have undergone continuous reorganization [59].…”

Section: Discussionmentioning

confidence: 99%

Carbon Emission Effects of Land Use in Chaobai River Region of Beijing–Tianjin–Hebei, China

Liu

et al. 2023

Land

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Beijing–Tianjin–Hebei, the main economic area in northern China, has seen significant changes in its regional economic and physical landscape as a result of the coordinated development strategy. Assessing the link between land use and land cover (LULC) change and carbon emissions in the Chaobai River region, which represents the growth of the Beijing–Tianjin–Hebei urban agglomeration, is crucial to achieve coordinated low-carbon development in this area. This study uses statistics from statistical yearbooks of Chinese provinces and cities along with land use change data to analyze the relationship between land use changes and carbon emissions in the Chaobai River region from 2001 to 2017 using dynamic land use attitudes and land use transfer matrices, combined with carbon emission factors based on the IPCC inventory method and carbon emission models for energy consumption. In addition, this study makes use of the LMDI model and geographical detectors to identify and assess the factors that influence changes in land use carbon emissions and the driving forces behind the regional differentiation of land use changes. The results show that: (1) The Chaobai River region’s predominant land use classes during the past 18 years have been agricultural land and construction land. In addition to the decrease in cropland and the increase in urban land, the land use patterns of other land classes also changed to a certain extent. (2) Carbon emissions from land use showed an increasing trend, from 6.1 × 106 tons in 2001 to 1.1 × 107 tons in 2017. (3) Carbon emission intensity, economic development level, land use efficiency, and construction land scale have a certain regularity in the evolution of carbon emissions, and economic development level has become the most important driving factor controlling the growth of land use carbon emissions. (4) Driving factors in different periods have different degrees of influence on land use change, among which socio-economic factors such as population density and GDP have the strongest explanatory power. In addition, the interactions of each factor mainly present a double factor enhancement. In the future, the Chaobai River region should be based on the coordinated development strategy and take the “double carbon” target as its guiding principle to promote the innovation of the regional development system and further achieve the optimization of the regional land use patterns.

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Section: Discussionmentioning

confidence: 99%

Carbon Emission Effects of Land Use in Chaobai River Region of Beijing–Tianjin–Hebei, China

Liu

et al. 2023

Land

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“…The establishment of Xiongan New District near Baoding City in 2017 further exacerbated the situation, inevitably occupying cultivated land and reducing the area available for food planting [81]. From 2010 to 2015, rapid urbanization around Cangzhou City, located in the low plain areas surrounding the Bohai Sea, led to a notable increase in NGPOWW in suburban regions (Figure 11) [82]. This prompted a considerable amount of winter wheat to be planted in the south as compensation, escalating the pressure on grain planting in the southern plains.…”

Section: Marginalization Of Winter Wheat Planting Caused By Ngpowwmentioning

confidence: 99%

Spatiotemporally Mapping Non-Grain Production of Winter Wheat Using a Developed Auto-Generating Sample Algorithm on Google Earth Engine

Zhang,

Sun,

Sun

2024

Remote Sensing

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Spatiotemporally mapping winter wheat is imperative for informing and shaping global food security policies. Traditional mapping methods heavily rely on sufficient and reliable samples obtained through labor-intensive fieldwork and manual sample collection. However, these methods are time-consuming, costly, and lack timely and continuous data collection. To address these challenges and fully leverage remote sensing big data and cloud computing platforms like Google Earth Engine (GEE), this paper developed an algorithm for Auto-Generating Winter Wheat Samples for mapping (AGWWS). The AGWWS utilizes historical samples to determine the optimal migration threshold by measuring Spectral Angle Distance (SAD), Euclidean Distance (ED), and Near-Infrared band Difference Index (NIRDI). This facilitates the auto-generation of winter wheat sample sets for the years 2000, 2005, 2010, 2015, and 2021. Approximately two-thirds of the samples were allocated for training, with the remaining one-third used for validating the mapping method, employing the One-Class Support Vector Machine (OCSVM). The Huang–Huai–Hai (HHH) Plain, a major winter wheat production region, was selected to perform the algorithm and subsequent analysis on. Different combinations of the hyper-parameters, gamma and nu, of the OCSVM based on the Gaussian Radial Basis Function Kernel were tested for each year. Following correlation analysis between the winter wheat area derived from the generated maps and the national statistical dataset at the city level, the map with the highest corresponding R2 was chosen as the AGWWS map for each year (0.77, 0.77, 0.80, 0.86, and 0.87 for 2000, 2005, 2010, 2015, and 2021, respectively). The AGWWS maps ultimately achieved an average Overall Accuracy of 81.65%. The study then explores the Non-Grain Production of Winter Wheat (NGPOWW) by analyzing winter wheat change maps from 2000–2005, 2005–2010, 2005–2010, and 2015–2021 in the HHH Plain. Despite an overall increase in the total planted area of winter wheat, the NGPOWW phenomena has led to concerning winter wheat planting marginalization. Compensatory winter wheat areas are notably situated in mountainous and suburban cultivated lands with low qualities. Consequently, despite the apparent expansion in planted areas, winter wheat production is anticipated to be adversely affected. The findings highlight the necessity for improved cultivated land protection policies monitoring the land quality of the compensation and setting strict quota limits on occupations.

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“…Increased energy consumption also enhances carbon emissions [29]. To mitigate carbon emissions, the "Outline of the B-T-H Coordinated Development Plan" [30] highlights the need for carbon sink enhancement measures, and the region should lead the way in coordinated development and reform, drive innovation-driven economic growth nationwide, and serve as a demonstration zone for ecological restoration [31]. Given the sound basis of synergistic development in the B-T-H region, it is an important pilot demonstration site to explore the mechanisms of climate impacts on carbon sinks [32].…”

Section: Introductionmentioning

confidence: 99%

Impact of Climate on the Carbon Sink Capacity of Ecological Spaces: A Case Study from the Beijing–Tianjin–Hebei Urban Agglomeration

Wang,

Zhang

et al. 2023

Land

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Climate plays a significant role in shaping ecosystem-level carbon sinks. Research on the mechanisms of climate impacts on carbon sinks can contribute to the achievement of carbon neutrality. Investigating the mechanisms by which climate impact on carbon sinks in ecological spaces in the Beijing–Tianjin–Hebei region, one of the most important urban clusters in China, is of great significance. This study employed spatial autocorrelation and econometric models to explore how various climatic factors impact net primary productivity (which is used to represent carbon sink capacity) on a spatial scale. We found an increasing trend in NPP across the Beijing–Tianjin–Hebei urban agglomeration from 2000 to 2020, with marked spatial clustering. Climatic factors exhibited the best fit with the spatial Durbin model, except for average annual precipitation. The remaining factors had significant effects on NPP, showing spatial spillover effects. Results also showed that the average annual temperature, evaporation, and relative humidity had positive impacts on NPP at a local scale but adverse effects at a regional scale. Average annual sunshine duration and the ground temperature had negative effects on NPP locally but promoted effects regionally. Furthermore, the average annual wind speed negatively impacted both local- and regional-scale NPP. This research provides insights into how climate affects carbon sinks on a small spatial scale, offering important references for making policy decisions and improving the accuracy of carbon cycling simulations.

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Spatial Evolution of Urban Expansion in the Beijing–Tianjin–Hebei Coordinated Development Region

Cited by 9 publications

References 61 publications

Carbon Emission Effects of Land Use in Chaobai River Region of Beijing–Tianjin–Hebei, China

Carbon Emission Effects of Land Use in Chaobai River Region of Beijing–Tianjin–Hebei, China

Spatiotemporally Mapping Non-Grain Production of Winter Wheat Using a Developed Auto-Generating Sample Algorithm on Google Earth Engine

Impact of Climate on the Carbon Sink Capacity of Ecological Spaces: A Case Study from the Beijing–Tianjin–Hebei Urban Agglomeration

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