Nothing Special   »   [go: up one dir, main page]

You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (6)

Search Parameters:
Keywords = Liaoyuan City

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
27 pages, 6255 KiB  
Article
Measurement and Spatio-Temporal Characteristics of High-Quality Development Efficiency in Metropolitan Areas: A Case Study of the Changchun Metropolitan Area
by Qiuyang Xu, Wenxin Liu and Lezhi Wu
Sustainability 2024, 16(11), 4581; https://doi.org/10.3390/su16114581 - 28 May 2024
Viewed by 911
Abstract
The concept of high-quality development (HQD) is characterized by its emphasis on efficiency, equity, and environmental sustainability. In the context of China’s new urbanization development, the metropolitan area plays a crucial role in facilitating and sustaining HQD. This study focuses on the Changchun [...] Read more.
The concept of high-quality development (HQD) is characterized by its emphasis on efficiency, equity, and environmental sustainability. In the context of China’s new urbanization development, the metropolitan area plays a crucial role in facilitating and sustaining HQD. This study focuses on the Changchun Metropolitan Area (CCMA) as a case study to measure the efficiency of high-quality development (HQDE) at the county level using the super-efficiency SBM model and spatial autocorrelation model. Additionally, we examine the spatio-temporal distribution characteristics of HQDE in terms of economy, innovation, coordination, greenness, openness, and sharing (EICGOS). The main findings are as follows: (1) The HQDE of the CCMA ranges from 0.7 to 0.8 with an initial rapid increase followed by a gradual decline; however, there are notable variations among different counties. (2) Regarding spatial structure within the metropolitan area, highest efficiency is observed in the half-hour living circle followed by the 2-h accessibility circle while lowest efficiency is found in the 1-h commuting circle. Over time, there is a declining trend in efficiency within core leading areas. (3) In terms of dimensions, CCMA demonstrates the highest level of economic development efficiency (EDE), whereas green development efficiency (GDE) exhibits lower levels compared to other dimensions. Furthermore, development efficiencies across all dimensions show a decline over time. (4) Spatially distributed patterns reveal significant agglomeration areas for HQDE within the CCMA region. High-high agglomeration areas are predominantly concentrated in the central region of Changchun and southern region of Liaoyuan while low-low agglomeration areas primarily exist in northwest Songyuan and specific counties within Changchun. To attain HQD of the CCMA, it is advisable to bolster the economic scale of the central city, mitigate developmental disparities between counties and cities, and expedite green transformations in old industrial cities. These findings offer a valuable point of reference for optimizing resource allocation at the metropolitan level and devising strategies to foster regional HQD. Full article
Show Figures

Figure 1

Figure 1
<p>Research framework.</p>
Full article ">Figure 2
<p>Efficiency measurement mechanism of HQD in CCMA.</p>
Full article ">Figure 3
<p>The study area (approval number GS(2020)4619).</p>
Full article ">Figure 4
<p>HQDE of 26 counties and districts from 2010 to 2020.</p>
Full article ">Figure 5
<p>(<b>a</b>) Efficiency values for spatial circles from 2010 to 2020 and (<b>b</b>) efficiency values for development dimensions from 2010 to 2020.</p>
Full article ">Figure 6
<p>Spatial distribution of HQDE in the CCMA from 2010 to 2020.</p>
Full article ">Figure 7
<p>Spatial distribution of HQDE in six development dimensions.</p>
Full article ">Figure 8
<p>Local spatial autocorrelation clustering diagram of HQDE in the CCMA from 2010 to 2020.</p>
Full article ">Figure 9
<p>LISA cluster graph of HQDE in six development dimensions.</p>
Full article ">
21 pages, 3086 KiB  
Article
Synergistic Patterns of Urban Economic Efficiency and the Economic Resilience of the Harbin–Changchun Urban Agglomeration in China
by Yang Ban, Ying Wang, Xiaohong Chen and Liuqing Wei
Sustainability 2023, 15(1), 102; https://doi.org/10.3390/su15010102 - 21 Dec 2022
Cited by 3 | Viewed by 1815
Abstract
Regional economic efficiency and resilience are necessary conditions for sustainable regional economic development, and urban agglomerations are the core carriers of regional economic development. Exploring the synergistic patterns between economic efficiency and economic resilience is crucial to the sustainable economic growth and development [...] Read more.
Regional economic efficiency and resilience are necessary conditions for sustainable regional economic development, and urban agglomerations are the core carriers of regional economic development. Exploring the synergistic patterns between economic efficiency and economic resilience is crucial to the sustainable economic growth and development of urban agglomerations and their surrounding regions. To measure the economic efficiency, economic resilience, and synergistic capacity of the Harbin–Changchun urban agglomeration from 2010 to 2019, the super-efficient SBM model, the entropy-TOPSIS model, and the Haken model are used. The economic efficiency of the Harbin–Changchun urban agglomeration shows a mild upward trend between 2010 and 2019, while its economic resilience shows a more stable upward trend. A distinct phased pattern of synergies exists between economic efficiency and economic resilience. In terms of the time trend, a “down-up-down” pattern emerges, while in terms of the spatial pattern, a dumbbell-shaped structure appears with “highs at the north and south and lows in the middle.” Combined synergy values are highest in the north and south of Qiqihar, Jilin, Siping, Liaoyuan, and Mudanjiang, followed by Harbin and Changchun; the values are lowest in the middle of Suihua, Daqing, and Songyuan. This study also proposes strategies to weaken inter-regional differentiation and to increase economic efficiency and economic resilience across cities in accordance with the actual situation. Full article
Show Figures

Figure 1

Figure 1
<p>Correlation diagram of economic resilience and economic efficiency.</p>
Full article ">Figure 2
<p>Location of the Harbin–Changchun urban agglomeration.</p>
Full article ">Figure 3
<p>(<b>a</b>) Kernel density estimates of urban economic efficiency in the Harbin–Changchun urban agglomeration; (<b>b</b>) kernel density estimates of urban resilience in the Harbin–Changchun urban agglomeration.</p>
Full article ">Figure 4
<p>Spatial distribution pattern of urban economic efficiency in the Harbin–Changchun urban agglomeration.</p>
Full article ">Figure 5
<p>Spatial distribution pattern of urban economic resilience in the Harbin–Changchun urban agglomeration.</p>
Full article ">Figure 6
<p>Kernel density analysis of synergistic values of the economic efficiency and economic resilience of cities in the Harbin–Changchun urban agglomeration.</p>
Full article ">Figure 7
<p>Spatial distribution of synergistic values of the economic efficiency and economic resilience of cities in the Harbin–Changchun urban agglomeration.</p>
Full article ">
15 pages, 4212 KiB  
Article
Remarkable Effects of Urbanization on Forest Landscape Multifunctionality in Urban Peripheries: Evidence from Liaoyuan City in Northeast China
by Jinghui Han, Yulin Dong, Zhibin Ren, Yunxia Du, Chengcong Wang, Guangliang Jia, Peng Zhang and Yujie Guo
Forests 2021, 12(12), 1779; https://doi.org/10.3390/f12121779 - 15 Dec 2021
Cited by 12 | Viewed by 2917
Abstract
Forest landscape multifunctionality (FLM) provides multiple benefits, such as climate regulation, water storage, and biodiversity maintenance. However, the external factors limiting FLM have not been fully identified, although addressing them could contribute to sustainable development. The present study aimed to identify and quantify [...] Read more.
Forest landscape multifunctionality (FLM) provides multiple benefits, such as climate regulation, water storage, and biodiversity maintenance. However, the external factors limiting FLM have not been fully identified, although addressing them could contribute to sustainable development. The present study aimed to identify and quantify the role of urbanization as an external factor that affects FLM. To this end, impervious area changes in Liaoyuan, China, were observed from 2000 to 2018, and 10 buffer zones at 500 m intervals were established outside the city. Within each buffer zone, we analyzed changes in forest landscape functions, including habitat maintenance, carbon sequestration, and water yield, as well as changes in the multifunctionality of their composition. The urbanization of Liaoyuan was significant in 2000–2018. The functions of the forest landscape became stronger and more stable as they were located further away from the urban edge. We refer to this pattern as the gradient effect of urbanization. Specifically, urbanization affected the investigated functions at a distance of 1000–2500 m. The FLM showed a more significant gradient effect of urbanization. The impact distance of urbanization on the FLM increased from 3000 m in 2000 to over 5000 m in 2018. This impact distance increased significantly whenever urbanization strengthened significantly (i.e., in 2005–2010 and 2015–2018). These findings are instructive for forest and urban managers working to achieve multiple Sustainable Development Goals. Full article
(This article belongs to the Special Issue Forest Biodiversity and Ecosystem Stability)
Show Figures

Figure 1

Figure 1
<p>The location and areal extent of Liaoyuan city. The urban boundary data are published by [<a href="#B28-forests-12-01779" class="html-bibr">28</a>].</p>
Full article ">Figure 2
<p>The flowchart shows the research framework and technical route of this study.</p>
Full article ">Figure 3
<p>Scatterplots show a method for quantifying the range of the impact of urbanization on landscape multifunctionality. (<b>a</b>) The conceptual change of multifunctionality index with the distance. (<b>b</b>) The derivative of the multifunctionality index.</p>
Full article ">Figure 4
<p>The spatiotemporal change in urban areas in Liaoyuan from 2000 to 2018. Panels (<b>a</b>–<b>f</b>) show the spatiotemporal change of impervious areas from 2000 to 2018. Panels (<b>g</b>,<b>h</b>) show the numerical change of impervious areas from 2000 to 2018.</p>
Full article ">Figure 5
<p>The spatiotemporal change of considered landscape functions in the urban periphery of Liaoyuan city from 2000 to 2015.</p>
Full article ">Figure 6
<p>The change in considered landscape functions in buffers of the urban periphery of Liaoyuan city from 2000 to 2018. (<b>a</b>) Habitat maintenance; (<b>b</b>) Carbon sequestration; (<b>c</b>) Water yield.</p>
Full article ">Figure 7
<p>The change of forest landscape multifunctionality in the urban periphery of Liaoyuan city from 2000 to 2018. Panels (<b>a-1</b>,<b>b-1</b>,<b>c-1</b>,<b>d-1</b>,<b>e-1</b>) show the change of multifunctionality index with the distance. Panels (<b>a-2</b>,<b>b-2</b>,<b>c-2</b>,<b>d-2</b>,<b>e-2</b>) show changes in the derivative of multifunctionality index with the distance.</p>
Full article ">
18 pages, 2557 KiB  
Article
A Three-Stage Hybrid Model for Space-Time Analysis of Water Resources Carrying Capacity: A Case Study of Jilin Province, China
by Tong Liu, Xiaohua Yang, Leihua Geng and Boyang Sun
Water 2020, 12(2), 426; https://doi.org/10.3390/w12020426 - 5 Feb 2020
Cited by 14 | Viewed by 3463
Abstract
Water shortage, water pollution, shrinking water area and water mobility are the main contents of the water resources crisis, which are widespread in the social and economic development of Jilin Province. In this paper, a three-stage hybrid model integrating evaluation, prediction and regulation [...] Read more.
Water shortage, water pollution, shrinking water area and water mobility are the main contents of the water resources crisis, which are widespread in the social and economic development of Jilin Province. In this paper, a three-stage hybrid model integrating evaluation, prediction and regulation is constructed by combining the load-balance method and the system dynamics method. Using this model, the current states of water resources carrying capacity (WRCC) in 2017 and the trend of water demand/available from 2018 to 2030 were obtained. Using the orthogonal test method, the optimal combination program of agricultural and industrial water efficiency regulation and water resources allocation was selected. The results show that the pressure of the human–water resources system in Changchun, Liaoyuan and Baicheng is greater than the support, and the other six cities are not overloaded. The water demand in Jilin Province and its nine cities will increase from 2018 to 2030, if the current socio-economic development pattern is maintained. Therefore, we change the water quantity carrying capacity index by controlling agriculture, industrial water efficiency and trans-regional water transfer. Compared with 2015, among the optimal program obtained, the change range of the water use per 10,000 RMB of agricultural output is (−5%, 25%), and the water use per 10,000 RMB of industrial added value is (−45%, −35%), and the maximum water transfer is 1.5 billion m3 per year in 2030. This study analyzes the development pattern of WRCC in the process of water conservancy modernization in Jilin Province and provides reference for other provinces to make the similar plan. Full article
Show Figures

Figure 1

Figure 1
<p>Location of Jilin Province.</p>
Full article ">Figure 2
<p>Framework of this work.</p>
Full article ">Figure 3
<p>The flow diagram of main variables in the sub-model of WRCC: (<b>a</b>) total population, (<b>b</b>) water demand, (<b>c</b>) water available.</p>
Full article ">Figure 4
<p>Carrying state of water resources.</p>
Full article ">Figure 5
<p>Simulation results of water demand/available in nine cities from 2018 to 2030.</p>
Full article ">
2891 KiB  
Article
The Urban Transition Performance of Resource-Based Cities in Northeast China
by Juntao Tan, Pingyu Zhang, Kevin Lo, Jing Li and Shiwei Liu
Sustainability 2016, 8(10), 1022; https://doi.org/10.3390/su8101022 - 13 Oct 2016
Cited by 59 | Viewed by 7350
Abstract
Resource-based cities face unique challenges when undergoing urban transitions because their non-renewable resources will eventually be exhausted. In this article, we introduce a new method of evaluating the urban transition performance of resource-based cities from economic, social and eco-environmental perspectives. A total of [...] Read more.
Resource-based cities face unique challenges when undergoing urban transitions because their non-renewable resources will eventually be exhausted. In this article, we introduce a new method of evaluating the urban transition performance of resource-based cities from economic, social and eco-environmental perspectives. A total of 19 resource-based cities in Northeast China are studied from 2003 to 2012. The results show that resource-based cities in Jilin and Liaoning provinces performed better than those in Heilongjiang province. Liaoyuan, Songyuan and Baishan were ranked as the top three resource-based cities; and Jixi, Yichun and Heihe were ranked last. Multi-resource and petroleum resource-based cities performed better than coal and forestry resource-based cities. We also analyzed the factors influencing urban transition performance using the method of the geographic detector. We found that capital input, road density and location advantage had the greatest effects on urban transition performance, followed by urban scale, remaining resources and the level of sustainable development; supporting policies and labor input had the smallest effects. Based on these insights, we have formulated several recommendations to facilitate urban transitions in China’s resource-based cities. Full article
(This article belongs to the Special Issue Urban Resilience and Urban Sustainability: From Research to Practice)
Show Figures

Figure 1

Figure 1
<p>Spatial distribution of the main resource-based cities in Northeast China.</p>
Full article ">Figure 2
<p>The value of the transition performance of resource-based cities in Northeast China.</p>
Full article ">Figure 3
<p>Economic, social and eco-environmental transition performance of the resource-based cities in Northeast China.</p>
Full article ">Figure 4
<p>Spatial distribution of classified influential factors: (<b>a</b>) Remaining resources; (<b>b</b>) Road density; (<b>c</b>) Location advantage; (<b>d</b>) Capital input; (<b>e</b>) Labor input; (<b>f</b>) Sustainable development level; (<b>g</b>) Urban scale; (<b>h</b>) Supporting policies.</p>
Full article ">
1828 KiB  
Article
Assessing Landscape Ecological Risk in a Mining City: A Case Study in Liaoyuan City, China
by Jian Peng, Minli Zong, Yi'na Hu, Yanxu Liu and Jiansheng Wu
Sustainability 2015, 7(7), 8312-8334; https://doi.org/10.3390/su7078312 - 29 Jun 2015
Cited by 117 | Viewed by 8704
Abstract
Landscape ecological risk assessment can effectively identify key elements for landscape sustainability, which directly improves human wellbeing. However, previous research has tended to apply risk probability, measured by overlaying landscape metrics to evaluate risk, generally lacking a quantitative assessment of loss and uncertainty [...] Read more.
Landscape ecological risk assessment can effectively identify key elements for landscape sustainability, which directly improves human wellbeing. However, previous research has tended to apply risk probability, measured by overlaying landscape metrics to evaluate risk, generally lacking a quantitative assessment of loss and uncertainty of risk. This study, taking Liaoyuan City as a case area, explores landscape ecological risk assessment associated with mining cities, based on probability of risk and potential ecological loss. The assessment results show landscape ecological risk is lower in highly urbanized areas than those rural areas, suggesting that not only cities but also natural and semi-natural areas contribute to overall landscape-scale ecological risk. Our comparison of potential ecological risk in 58 watersheds in the region shows that ecological loss are moderate or high in the 10 high-risk watersheds. The 35 moderate-risk watersheds contain a large proportion of farmland, and the 13 low-risk watersheds are mainly distributed in flat terrain areas. Our uncertainty analyses result in a close range between simulated and calculated values, suggesting that our model is generally applicable. Our analysis has good potential in the fields of resource development, landscape planning and ecological restoration, and provides a quantitative method for achieving landscape sustainability in a mining city. Full article
(This article belongs to the Special Issue Landscape and Sustainability)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Location of Liaoyuan City, China.</p>
Full article ">Figure 2
<p>Extracted watersheds based on DEM in Liaoyuan City, China. (Note: R-River, U-Upstream, M-Midstream, D-Downstream).</p>
Full article ">Figure 3
<p>Spatial distribution of four types of disturbance: (<b>a</b>) Landscape disturbance; (<b>b</b>) Mining disturbance; (<b>c</b>) Settlement disturbance; (<b>d</b>) Road disturbance.</p>
Full article ">Figure 4
<p>Ecological disturbance and vulnerability of watersheds in Liaoyuan City, China.</p>
Full article ">Figure 5
<p>Landscape ecological risk probability and ecological loss in Liaoyuan City, China.</p>
Full article ">Figure 6
<p>Landscape ecological risk of watersheds in Liaoyuan City, China.</p>
Full article ">Figure 7
<p>Landscape ecological risk zoning and total area of land use types in each grade of landscape ecological risk in Liaoyuan City, China (Note: R means ecological risk, P means ecological risk probability, and L means ecological loss).</p>
Full article ">Figure 8
<p>Simulated landscape ecological risk under low or high uncertainty of watersheds in Liaoyuan City, China.</p>
Full article ">Figure 9
<p>Variance contribution rate of sensitive factors under low or high uncertainty.</p>
Full article ">Figure 10
<p>Most sensitive factors in assessing landscape ecological risk under low or high uncertainty of watersheds in Liaoyuan City, China.</p>
Full article ">
Back to TopTop