How Do Ecosystem Services Affect Poverty Reduction Efficiency? A Panel Data Analysis of State Poverty Counties in China

Cao, Peng; Ouyang, Xiao; Jun, Xu

doi:10.3390/ijerph19031886

Cited by 7 publications

(2 citation statements)

References 37 publications

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“…Analysis of the influence of this spatial aspect is important to study in research on the number of poor people because there are differences in characteristics in determining the level of poverty in each region (Cao, Ouyang, & Xu, 2022;Do et al, 2021). The differences in the influencing factors in each topography indicate the influence of local conditions in a particular area of determining significant factors in poverty modeling (Tu, Ha, Wang, & Liu, 2020;Yulianto, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling and mapping the poverty levels with applied spatial regression model in South Sulawesi province of Indonesia

Didiharyono,

Syukri,

Fisu

et al. 2024

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The focus of this study is to model the poverty rate using a statistical approach and then carry out a spatial mapping of the poverty rate in regencies and cities in South Sulawesi Province. This study is important to research so that it becomes a reference for the government in formulating policy analysis on poverty alleviation and its sustainability. The method used is the spatial regression model with cross-sectional data, using R software as a tool for analyzing research data. The findings show that the poverty levels in the regencies and cities of South Sulawesi province are randomly distributed and have a dispersed pattern. The significance of the response variable under analysis has an impact on this. In addition, the best model was obtained, namely the Spatial Error Model (SEM) model with the smallest p-value and a confidence level of 86%. The results of the hot-spot and cold-spot analysis show that the concentration of poverty levels is still focused on the southern region in the hot-spot area (red color), namely Bantaeng Regency, Jeneponto Regency, Takalar Regency, Gowa Regency, Makassar City, Maros Regency, Sinjai Regency, and Barru Regency. The implications for the community in the hot-spot category area are that special attention is needed from the government and stakeholders to be directly involved in improving the poverty level that occurs in the community through sustainable poverty alleviation programs.

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Section: Literature Reviewmentioning

confidence: 99%

Modelling and mapping the poverty levels with applied spatial regression model in South Sulawesi province of Indonesia

Didiharyono,

Syukri,

Fisu

et al. 2024

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“…Zhou et al [ 31 ] explored the relationship between recreational services and tourists’ well-being, while Plutino et al [ 32 ] provided a framework for ecosystem services, regulating services, pollination and human health. Istanbuly et al [ 33 ] addressed the relationship between socioeconomic variables and soil erosion in Polish catchments, while Cao et al [ 34 ] evaluated the interaction between poverty reduction and ecosystem services in poverty counties, something essential to promote rural revitalisation strategy and the construction of an ecological civilisation. Finally, Sena and Ebi [ 35 ] highlighted the pressures and impacts of desertification, land degradation and drought on human health.…”

Section: Introductionmentioning

confidence: 99%

Restoring Coal Mining-Affected Areas: The Missing Ecosystem Services

Krzemień

Fernández

et al. 2022

IJERPH

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Multi-criteria decision analysis and cost-benefit analysis, either individually or in combination, have been used as the preferred tools to develop ecosystem services valuation, presenting significant discrepancies and variations between the calculated values. To counteract this problem, a new framework was developed based on a hierarchical weighting of the non-provisioning ecosystem services, using biodiversity as the reference ecosystem service since it is the easiest to apprehend. Their monetisation was made using the average price of EU carbon dioxide emission allowances during 2019 and 2020, obtaining reasonable and comparable results in line with what was expected for the study region. However, the revised EU Emissions Trading System Directive, which will apply from 2021–2030, generated a price escalation of carbon allowances, making it necessary to adjust or rethink the proposed framework. To achieve this goal, the paper proposes the introduction of new vectors or “missing ecosystem services” to counterbalance efforts to eliminate carbon dioxide emissions without necessarily removing humans from the equation: welfare and human health. As the linkages regarding ecosystem health, ecological restoration and human health are not well known, only welfare was incorporated into the framework. The results were highly satisfactory, in line with what was expected for the study region and the ones obtained before the price escalation of carbon allowances that started in 2021.

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Nonlinear influence of construction land on poverty reduction efficiency in Chinese poor counties: new evidence using a panel threshold model

Wang

Cao

Ouyang

2022

Environ Dev Sustain

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How Do Ecosystem Services Affect Poverty Reduction Efficiency? A Panel Data Analysis of State Poverty Counties in China

Cited by 7 publications

References 37 publications

Modelling and mapping the poverty levels with applied spatial regression model in South Sulawesi province of Indonesia

Modelling and mapping the poverty levels with applied spatial regression model in South Sulawesi province of Indonesia

Restoring Coal Mining-Affected Areas: The Missing Ecosystem Services

Nonlinear influence of construction land on poverty reduction efficiency in Chinese poor counties: new evidence using a panel threshold model

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