Simulation and Analysis of Urban Production–Living–Ecological Space Evolution Based on a Macro–Micro Joint Decision Model

Tao, Yuanyuan; Wang, Qianxin; Zou, Yan

doi:10.3390/ijerph18189832

Cited by 6 publications

(7 citation statements)

References 38 publications

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“…Relevant actors at this scale include individuals, households, social networks and specific organizations, such as workplaces (Oliveira et al, 2020). At the micro scale, these actors often have more power than at the macro scale to decide or control consumption behaviors in relation to their own material possessions, environment, and non-material conditions (Jørgensen, 2012;Tao et al, 2021).…”

Section: Systems Influencing Urban Consumptionmentioning

confidence: 99%

How Does Socio-Technical Lock-In Cause Unsustainable Consumption in Cities? A Framework and Case Study on Mobility in Bangkok

Truong

Trencher

Matsubae

2022

Front. Sustain. Cities

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Consumption of raw materials, energy, manufactured goods, and services is increasingly concentrated in cities, as urbanization accelerates globally. Such consumption is influenced by complex interactions arising between the various socio-technical and natural systems that make up cities. To improve understanding of the interlinked factors that can perpetuate—or “lock-in”—unsustainable consumption, we build an explanatory framework that conceptually joins the literature on socio-technical systems and on urban consumption. Two questions guide our study: (1) What are the principal socio-technical systems in cities that influence consumption behavior? (2) How do these systems interact to lock urban dwellers into unsustainable consumption behavior? The resulting framework incorporates theories of socio-technical lock-in with factors relating to both “structure” and “agency” in consumption literature. Specifically, it describes the influence and interactions of physical, non-physical, and human systems on two interlinked scales: macro-scale (structure and collectively shared conditions) and micro-scale (agency and individually shaped conditions). To demonstrate the practical value of this framework, we apply it to a case study on mobility in Bangkok, Thailand. This allows us to systematically identify the interlinked mechanisms contributing to the growing dependence on and lock-in to individually owned passenger vehicles. Our study thus provides a comprehensive understanding of the multiplex drivers of consumption behavior, taking into account both structure and agency. The framework also provides a tool for other scholars to empirically identify lock-in mechanisms that hamper the adoption of more sustainable consumption behavior in other sectors and geographies.

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Section: Systems Influencing Urban Consumptionmentioning

confidence: 99%

How Does Socio-Technical Lock-In Cause Unsustainable Consumption in Cities? A Framework and Case Study on Mobility in Bangkok

Truong

Trencher

Matsubae

2022

Front. Sustain. Cities

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“…The green coverage rate of urban built-up areas also increased from 33.1% in 1995 to 43.1% in 2020. After rapid urbanization [75] and subsequent ecological construction transformation in this resource-exhausted city [76], Xuzhou has become a UN Habitat Award city, but there remains an obvious heat island effect in the main urban area, and the UHI effect is increasing year by year [77,78]. The scope of this study was defined as the main urban area of Xuzhou city determined by the Xuzhou City Master Plan (2007-2020) (revised in 2017), which includes Gulou District, Quanshan District, Yunlong District, Tongshan District, Xincheng District and the Economic and Technological Development Zone, with an area of approximately 573.2 km 2 and an average terrain elevation of 35.1 m in the study area (Figure 1a).…”

Section: Overview Of the Study Areamentioning

confidence: 99%

Evaluation of the Thermal Environmental Effects of Urban Ecological Networks—A Case Study of Xuzhou City, China

2022

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Urban heat islands (UHIs) constitute an important ecological problem in cities. Ecological space has a positive effect on UHI mitigation, which can be effectively organized in the form of ecological networks. In this study, the framework for structural UHI improvement based on ecological networks considering the source-corridor model is proposed to examine the spatial threshold of the thermal effect of ecological network factors. Additionally, the cooling mechanism of each constituent element in the ecological network context is further explored. The results demonstrate that (1) an obvious cold and heat island spatial aggregation distribution exists in the Xuzhou main urban area, and land of the same land use type exhibits the dual thermal environmental properties of cold and heat islands through its spatial distribution and characteristics. Ecological space is the main bearing area of cold islands. (2) The ecological network in the main urban area of Xuzhou city occurs at a moderately complex level, and the overall network efficiency is acceptable; the network connectivity is low, while the network loop distribution is uneven. (3) Ecological networks represent an effective spatial means to improve overall UHI patterns. The ecological source area cooling threshold is 300 m, and the optimal threshold is 100 m, while the ecological corridor width threshold is 500 m and 60 m, respectively. (4) Within the optimal threshold in the context of ecological networks, the temperature of ecological sources in category G land is influenced by NDBI and FVC; ecological corridors are mainly influenced by NDBI. The results can provide a quantitative basis for urban ecological network planning considering UHI improvement and a reference for urban thermal environment research within different ecological substrates and planning and control systems in other countries and regions worldwide.

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“…At present, relatively fruitful results have been achieved in the field of the identification of PLEF and the pattern reconstruction of PLES. The research content mainly focused on the connotation of PLEF (Rudolf, 2005;Huang et al, 2020), the identification of the PLES pattern (Zou et al, 2020;Chen et al, 2021;Duan et al, 2021;Wu et al, 2021), the evolution of the PLES pattern (Deng and Yang, 2021;Tao et al, 2021;Wei et al, 2021;Zhao et al, 2021), and the optimization of its spatial pattern (Tian et al, 2020;Yin et al, 2021). Generally speaking, the identification of PLES can be realized by two mainstream methods: Land Use Classification (LUC) (Liu et al, 2017;Li et al, 2021;Lin et al, 2021) and Evaluation Index System (EIS) (Liao et al, 2020;Yang et al, 2020;Zou et al, 2020;Wei et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Spatial pattern optimization of rural production-living-ecological function based on coupling coordination degree in shallow mountainous areas of Quyang County, Hebei Province, China

et al. 2023

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IntroductionThe shallow mountainous area in Hebei province is a crucial part of the ecological security barrier and regional ecological conservation construction in the Beijing-Tianjin-Hebei (BTH) region. In recent years, the contradictions in the development of the rural “production-living-ecological” function (PLEF) in shallow mountainous areas are prominent, so optimize its spatial pattern is beneficial to rural sustainable development. But there are significant problems in the existing research, such as the lack of fine-scale research and effective guidance for rural PLEF. Based on this, this study takes Quyang County as an example, starts from the perspective of PLEF coordinated development, finally puts forward the optimization strategy of rural production-living-ecological space (PLES) pattern by evaluating rural PLEF and its coupling co-scheduling.MethodsThis study first fused multi-source data such as POI and remote sensing images to build a comprehensive evaluation system of rural PLES, combined with entropy weight method and analytic hierarchy process to give weight to various indicators, and calculated the PLEF distribution of Quyang County on the 300 × 300m grid scale. Then the collaborative development of PLEF is measured by coupling coordination degree model. Finally, according to PLEF and its coupling and coordination, the functional space types are divided according to the principles of coordinated development and ecological optimization, and the optimization strategy of PLES pattern is proposed on the village scale.Results(1) The spatial distribution of PLEF in Quyang County is significantly different, and the order of functional intensity is: ecological space (ES) > production space (PS) > living space (LS). (2) The PLEF coupling coordination degree generally presents the spatial distribution characteristics of “low in the north and high in the south”, which is highly related to its topographic features. The high-value areas are mainly spread over southern plains with developed economy and rich ecological resources, while the low-value areas are located in the northern mountains and the central hills. (3) On the grid scale, the PLES pattern is identified as six types: production-living-ecological balance space (PLEBS), production-living space (PLS), production-ecological space (PES), living-ecological space (LES), ES and PS. Among them, the proportion of PLEBS and ES is larger. (4) On the village scale, it is suggested that PLEBS villages further emphasize high-quality coordinated development; ecological function leading optimization type (EFLOT) villages adhere to ecological priorities and ensure the development of ecological space functions; villages with composite functions should combine their own advantages and the spatial characteristics of the surrounding countryside, optimize and control infrastructure configuration, industrial structure, ecological protection and other aspects of classification, overcome shortcomings and improve the coordination of the PLEF.DiscussionBased on previous studies, this paper explored and improved the research scale, analysis methods, evaluation indexes and optimization ideas in the field of rural PLEF. Therefore, the results can guide for the high-quality coordinated development of territorial space and rural revitalization construction of counties in shallow mountainous areas.

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Simulation and Analysis of Urban Production–Living–Ecological Space Evolution Based on a Macro–Micro Joint Decision Model

Cited by 6 publications

References 38 publications

How Does Socio-Technical Lock-In Cause Unsustainable Consumption in Cities? A Framework and Case Study on Mobility in Bangkok

How Does Socio-Technical Lock-In Cause Unsustainable Consumption in Cities? A Framework and Case Study on Mobility in Bangkok

Evaluation of the Thermal Environmental Effects of Urban Ecological Networks—A Case Study of Xuzhou City, China

Spatial pattern optimization of rural production-living-ecological function based on coupling coordination degree in shallow mountainous areas of Quyang County, Hebei Province, China

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