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Enregistrement W4405008357 · doi:10.3724/j.fjyl.202312110554

Research Progress in Supply and Demand of Ecosystem Services in Blue and Green Spaces in Urban Communities

2024· article· en· W4405008357 sur OpenAlexaboutno aff
Chuhan Zhang, Guoyu Wang

Notice bibliographique

RevueLandscape Architecture · 2024
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueLand Use and Ecosystem Services
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésEcosystem servicesSupply and demandOn demandEcosystemBusinessUrban ecosystemGeographyEnvironmental resource managementEcologyEnvironmental scienceEconomicsUrban planningBiologyCommerceMicroeconomics

Résumé

récupéré en direct d'OpenAlex

Objective Community blue and green space (BGS), such as parks, greenways, and pocket parks, are vital components of urban living environments, providing residents with opportunities to experience nature and benefit from ecosystem services (ES). This research aims to explore the main factors influencing the supply and demand of ES in community BGS, identify research hotspots and methodologies, and develop a supply – demand evaluation framework to enhance the effectiveness of ES in these spaces. Methods A comprehensive literature review is conducted using the Web of Science (WoS) and China National Knowledge Infrastructure (CNKI) databases, with a focus on research published between 2010 and 2023. Keywords include ES types relevant to community scales, community BGS types, and element characteristics. Upon exclusion of unrelated research, 455 papers are finally selected. Citespace software is employed for bibliometric and hotspot analyses. Additionally, 62 empirical case studies closely related to community BGS are systematically reviewed to extract key factors influencing the supply and demand of various ES types, with a focus on quantification methods and significant influences. The supply – demand relationship is evaluated from the two perspectives of supply – demand level and effectiveness, based on which a cascading research framework aimed at achieving supply – demand matching goals is developed. Results The analysis reveals a global increase in research on community BGS and ES since 2012, with significant growth after 2017, reflecting a growing interest in optimizing ES in urban communities. Research hotspots vary by region. In China, research focuses on spatial distribution equity and residents’ health and well-being, emphasizing BGS layout optimization. In the United States, research emphasizes environmental justice and the impact of built environments on ES, mainly aiming to explore relationships between socioeconomic factors and the supply and demand of community green space. European countries concentrate on urban green spaces and biodiversity conservation, while Australia and Canada focus on sustainable urban planning and BGS management. Different types of community BGS exhibit varying research emphases on ES. Community parks and green spaces are the most popular research subjects, particularly concerning cultural services like recreation and social interaction. Research on support and regulatory services often focuses on linear green spaces, vertical greening, and vegetation elements, utilizing ecological modeling methods (e.g., ENVI-met, SWMM) for supply quantification. Key findings indicate that the supply side of ES is primarily influenced by biophysical characteristics such as the area, type, and structure of vegetation, and the availability and quality of physical spaces. Larger and higher-quality green spaces and the presence of water bodies can enhance ES provision. Cultural services are additionally influenced by accessibility factors, including vegetation type, park facility distribution, and microclimate conditions. On the demand side, objective demand is influenced by demographic and socioeconomic factors like population size and density, GDP per capita, and points of interest (POI) density. Subjective demand, especially for cultural services, is shaped by residents’ activity patterns, travel characteristics, personal preferences, and satisfaction levels. Socio-demographic factors such as gender, age, income, occupation, and education level affect subjective preferences and perceived needs. The supply – demand cascading research framework developed in this research emphasizes the connection between the biophysical attributes of supply spaces and the socioeconomic attributes of demand spaces. This framework facilitates the identification and evaluation of supply – demand mismatches, guiding the optimization of resource allocation and spatial utilization in community BGS. By applying this framework, areas with high demand but low supply can be targeted for improvements, thus enhancing ES provision where it is most needed. Conversely, areas with excess supply can be managed strategically for future development and community enhancement, thus ensuring efficient resource allocation and maximizing benefits for residents. Conclusion The findings provide valuable insights for systematically studying the supply – demand relationship of ES in community BGS. The proposed framework offers a scientific basis for optimizing resource allocation and spatial efficiency, which can contribute to the construction of sustainable community environments and the improvement of residents’ quality of life. Future research should focus on identifying and evaluating the fragmented characteristics of community BGS, especially in urban areas with high spatial heterogeneity; exploring the diverse needs of different population groups within communities by considering demographic characteristics, socioeconomic status, and cultural preferences; and evaluating supply – demand effectiveness at micro and meso scales by integrating multiple data sources and employing advanced analytical methods. Moreover, participatory approaches are important in understanding and meeting residents’ needs. Engaging community members in BGS planning and management can ensure the alignment of services with their preferences, thereby enhancing their well-being. This collaborative approach can help build more sustainable and resilient communities, thus maximizing ES benefits and mitigating the negative impacts of urbanization. In conclusion, this research provides a robust framework for evaluating and improving the supply – demand dynamics of ES in community BGS. The insights gained can guide policymakers, urban planners, and community stakeholders in making informed decisions that promote sustainable development and enhance the quality of life for urban residents. Continuous improvement of ES in community BGS will contribute significantly to achieving broader urban sustainability goals and creating livable, healthy, and vibrant communities.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,008
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: aucune
Score de désaccord entre enseignants0,016
Score d'incertitude au seuil0,032

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,008
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0080,018
Études des sciences et des technologies0,0010,001
Communication savante0,0050,007
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0070,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,008
Tête enseignante GPT0,242
Écart entre enseignants0,233 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2024
Routes d'admission1
Résumé présentoui

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