Analyzing the critical factors influencing residents’ willingness to pay for old residential neighborhoods renewal: insights from Nanjing, China

Xu, Xiaoxiao; Shi, Fada; Zhu, Jiajun

doi:10.1007/s10668-023-04446-9

Cited by 3 publications

(1 citation statement)

References 47 publications

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“…Excessive levels of soil heavy metals not only impede soil productivity but also pose risks to human and animal health through their inhalation and ingestion pathways [10,11]. Thus, understanding the spatial distribution of soil heavy metal contents and evaluating the status of soil heavy metal pollution are crucial for safeguarding agricultural production and protecting human health [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Spatial Distribution Prediction of Soil Heavy Metals Based on Random Forest Model

Nie,

Chen,

Sun

et al. 2024

Sustainability

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Mastering the spatial distribution of soil heavy metal content and evaluating the pollution status of soil heavy metals is of great significance for ensuring agricultural production and protecting human health. This study used a machine learning model to study the spatial distribution of soil heavy metal content in a coastal city in eastern China. Having obtained six soil heavy metal contents, including Cr, Cd, Pb, As, Hg, and Ni, environmental variables such as precipitation, soil moisture, and population density were selected. Random forest (RF) was used to model the spatial distribution of soil heavy metal content. The research findings indicate that the RF model demonstrates a robust predictive capability in discerning the spatial distribution of soil heavy metals, and environmental factor variables can explain 60%, 52.3%, 53.5%, 63.1%, 61.2%, and 51.2% of the heavy metal content of Cr, Cd, Pb, As, Hg, and Ni in soil, respectively. Among the chosen environmental variables, precipitation and population density exert notable influences on the predictive outcomes of the model. Specifically, precipitation exhibits the most substantial impact on Cr and Ni, whereas population density emerges as the primary determinant for Cd, Pb, As, and Hg. The RF prediction results show that Cr and Ni in the study area are less affected by human activities, while Cd, Pb, As, and Hg are more affected by human industrial and agricultural production. Research has shown that using RF models for predicting soil heavy metal distributions has certain significance.

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Section: Introductionmentioning

confidence: 99%

Spatial Distribution Prediction of Soil Heavy Metals Based on Random Forest Model

Nie,

Chen,

Sun

et al. 2024

Sustainability

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The premium of the old neighborhood upgrading projects in China: evidence from Hefei

Yongbin,

Hai

2024

J Hous and the Built Environ

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Willingness to Pay for Community-based Health Insurance: A Systematic Review and Analysis of Factors Influencing Participation and Implications for Healthcare Financing

Bhojak,

Dodiya,

Mathur

et al. 2024

South Asian Journal of Business and Management Cases

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Willingness to pay (WTP) for community-based health insurance (CBHI) is a crucial factor in understanding individuals’ attitudes and behaviours towards participating in insurance schemes. This article aims to examine the concept of WTP for CBHI and its implications for healthcare financing and accessibility. The research gives an organized and retrospective view of the antecedents, decisions and outcomes (ADO) of CBHI using a systematic review. The article also provides an overview of the theories, contexts and methods (TCM) employed to gain this understanding. The analysis concludes by identifying existing knowledge gaps and suggesting relevant ideas for future WTP and CBHI research. This article contributes to the existing body of knowledge on WTP for CBHI and provides insights for policymakers and researchers seeking to enhance the sustainability and effectiveness of CBHI programmes.

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Analyzing the critical factors influencing residents’ willingness to pay for old residential neighborhoods renewal: insights from Nanjing, China

Cited by 3 publications

References 47 publications

Spatial Distribution Prediction of Soil Heavy Metals Based on Random Forest Model

Spatial Distribution Prediction of Soil Heavy Metals Based on Random Forest Model

The premium of the old neighborhood upgrading projects in China: evidence from Hefei

Willingness to Pay for Community-based Health Insurance: A Systematic Review and Analysis of Factors Influencing Participation and Implications for Healthcare Financing

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