Quantifying spatial non-stationarity in the relationship between landscape structure and the provision of ecosystem services: An example in the New Zealand hill country

Tran, Duy X.; Pearson, Diane; Palmer, Alan; Lowry, John H.; Gray, David A.; Dominati, Estelle

doi:10.1016/j.scitotenv.2021.152126

Cited by 54 publications

(26 citation statements)

References 79 publications

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“…Achieving the ESV improvement implies a reduction in urban per capita disposable income and an increase in the agricultural output, which is impossible due to the promotion of urban livelihood and urbanisation processes. In addition, AI also shows a significant negative correlation with ESV, which conforms to the results from the study by Tran et al (2022) in the Manawatu-Wanganui region of the North Island, New Zealand, and the study by Rolo et al (2021) in 12 rural regions across 9 European countries, including Mediterranean, Atlantic, Boreal and Continental [56,57]. The relationship between ESV and landscape pattern have all been diagnosed.…”

Section: Discussionsupporting

confidence: 89%

“…When varying coefficients are produced, the local benefits of improved ESVs can be obtained through the AI adjustment. Inspiringly, the adjustment of the urban landscape through the spatial structure and function seems to be feasible in improving ESs [53,56]. Furthermore, it is true that in ecosystem benefits, it is not enough to take landscape pattern, rural development and living standards as the influencing factors.…”

Section: Discussionmentioning

confidence: 99%

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Integrating Landscape Pattern into Characterising and Optimising Ecosystem Services for Regional Sustainable Development

Zeng

Liu

et al. 2022

Land

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Humans benefit from ecosystem services (ES) and profoundly influence the ecosystem in rapid urbanisation and large-scale urban sprawl contexts, especially at the landscape level. However, the impacts of landscape pattern, the driving mechanism of sub-ES and the spatially explicit regional optimisation, have been largely ignored. In response, to the present paper explores two primary aspects: the relationship among ES, landscape pattern, urban income and agricultural output, and the regional governance of optimised ES values (ESV), using the Wuhan urban agglomeration as a case study area. The survey method is employed in obtaining the adjusted magnitude matrix of land use and ecosystem services. Spatial regression analyses are conducted on each ES, including food provision, climate regulation and soil maintenance, with socio-economic indicators and landscape pattern index as explanatory variables. Finally, geographically weighted regression and scenario analyses are conducted on each sub-ESV to generate adjusted coefficients in each county for ESV regulation. The results show that urban per capita disposable income and agricultural output significantly contribute to ESV change, with the former being negative and the latter being positive. A highly aggregated landscape also produces reduced ESV, particularly in soil maintenance and gas and climate regulation. We summarise the ESV in 2020 and in the period after adjustment in different administrative counties. Provision, regulation and culture ecosystem benefits substantially increase when attempts are made to lower the landscape aggregation pattern by 1%. In general, counties and county-level cities have the largest ESV, with food provision as the optimum ecosystem benefit. Districts in the capital city show an immense growth in provision and regulation, and county-level cities show the highest growth rate in cultural service. Integrating the landscape pattern into characterising and optimising ES, provides references for regional governance on land-use planning and socio-economic development, which is vital to sustainable regional development.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Integrating Landscape Pattern into Characterising and Optimising Ecosystem Services for Regional Sustainable Development

Zeng

Liu

et al. 2022

Land

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“…We adopted multi-scale geographically weighted regression (MGWR) to explore the spatial distribution and scales of influencing factors in ASDRs among 204 countries in 2019. The traditional GWR model applies a constant bandwidth to illustrate the impact scales of different factors, which ignores the diversity of impact scales and does not align with the facts (38). MGWR is an extension of the GWR model that allows for exploring the associations at varying spatial scales and achieves that by using a varying bandwidth rather than a single, and constant bandwidth for the entire area, so as to provide more credible estimation results and the diverse impact scales of each factor (39).…”

Section: Discussionmentioning

confidence: 99%

The Global Burden of Type 2 Diabetes Attributable to Tobacco: A Secondary Analysis From the Global Burden of Disease Study 2019

Bai

Shi

et al. 2022

Front. Endocrinol.

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ObjectivesGrowing epidemiological studies have reported the relationship between tobacco and health loss among patients with type 2 diabetes (T2D). This study aimed to explore the secular trend and spatial distribution of the T2D burden attributable to tobacco on a global scale to better understand regional disparities and judge the gap between current conditions and expectations.MethodsAs a secondary analysis, we extracted data of tobacco-attributable T2D burden from the 2019 Global Burden of Disease Study (GBD). Joinpoint regression was adopted to determine the secular trend of age-standardized rates (ASR), with average annual percentage change (AAPC). Gaussian process regression (GPR) was used to explore the average expected relationship between ASRs and the socio-demographic index (SDI). Spatial autocorrelation was used to indicate if there is clustering of age-standardized DALY rate (ASDR) with Moran’s I value. Multi-scale geographically weighted regression (MGWR) was to investigate the spatial distribution and scales of influencing factors in ASDR attributable to tobacco, with the regression coefficients for each influencing factor among 204 countries.ResultsTobacco posed a challenge to global T2D health, particularly for the elderly and men from lower SDI regions. For women, mortality attributable to secondhand smoke was higher than smoking. A downward trend in age-standardized mortality rate (ASMR) of T2D attributable to tobacco was observed (AAPCs= -0.24; 95% CI -0.30 to -0.18), while the ASDR increased globally since 1990 (AAPCs= 0.19; 0.11 to 0.27). Oceania, Southern Sub-Saharan Africa, and Southeast Asia had the highest ASMRs and ASDRs, exceeding expectations based on the SDI. Also, “high-high” clusters were mainly observed in South Africa and Southeast Asian countries, which means a high-ASDR country is surrounded by high-ASDR neighborhoods in the above areas. According to MGWR model, smoking prevalence was the most sensitive influencing factor, with regression coefficients from 0.15 to 1.80.ConclusionThe tobacco-attributable burden of T2D should be considered as an important health issue, especially in low-middle and middle-SDI regions. Meanwhile, secondhand smoke posed a greater risk to women. Regional disparities existed, with hot spots mainly concentrated in South Africa and Southeast Asian countries.

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“…Compared with the classical geographically weighted regression model (GWR), the MGWR model was a flexible regression model ( 28 ). Each regression coefficient was obtained based on local regression, and the bandwidth is specific.…”

Section: Methodsmentioning

confidence: 99%

Surface ozone pollution in China: Trends, exposure risks, and drivers

Wu²,

Li³

et al. 2023

Front. Public Health

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IntroductionWithin the context of the yearly improvement of particulate matter (PM) pollution in Chinese cities, Surface ozone (O3) concentrations are increasing instead of decreasing and are becoming the second most important air pollutant after PM. Long-term exposure to high concentrations of O3 can have adverse effects on human health. In-depth investigation of the spatiotemporal patterns, exposure risks, and drivers of O3 is relevant for assessing the future health burden of O3 pollution and implementing air pollution control policies in China.MethodsBased on high-resolution O3 concentration reanalysis data, we investigated the spatial and temporal patterns, population exposure risks, and dominant drivers of O3 pollution in China from 2013 to 2018 utilizing trend analysis methods, spatial clustering models, exposure-response functions, and multi-scale geographically weighted regression models (MGWR).ResultsThe results show that the annual average O3 concentration in China increased significantly at a rate of 1.84 μg/m3/year from 2013 to 2018 (160 μg/m3) in China increased from 1.2% in 2013 to 28.9% in 2018, and over 20,000 people suffered premature death from respiratory diseases attributed to O3 exposure each year. Thus, the sustained increase in O3 concentrations in China is an important factor contributing to the increasing threat to human health. Furthermore, the results of spatial regression models indicate that population, the share of secondary industry in GDP, NOx emissions, temperature, average wind speed, and relative humidity are important determinants of O3 concentration variation and significant spatial differences are observed.DiscussionThe spatial differences of drivers result in the spatial heterogeneity of O3 concentration and exposure risks in China. Therefore, the O3 control policies adapted to various regions should be formulated in the future O3 regulation process in China.

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Quantifying spatial non-stationarity in the relationship between landscape structure and the provision of ecosystem services: An example in the New Zealand hill country

Cited by 54 publications

References 79 publications

Integrating Landscape Pattern into Characterising and Optimising Ecosystem Services for Regional Sustainable Development

Integrating Landscape Pattern into Characterising and Optimising Ecosystem Services for Regional Sustainable Development

The Global Burden of Type 2 Diabetes Attributable to Tobacco: A Secondary Analysis From the Global Burden of Disease Study 2019

Surface ozone pollution in China: Trends, exposure risks, and drivers

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