Spatial disparity dynamics of ecosystem service values and GDP in Shaanxi Province, China in the last 30 years

Li, Tianhong; Ding, Yao

doi:10.1371/journal.pone.0174562

Cited by 15 publications

(14 citation statements)

References 49 publications

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“…Being aware of the contribution of ESV to green GDP in different regions can help the government develop different policies for different regions. Li and Ding [13] evaluated the variations of ESV and GDP for Shananxi province from the 1980s to 2010 and found that disparities in GDP were much higher than those of ESV, which is consistent with our results.…”

Section: Conclusion and Discussionsupporting

confidence: 89%

“…It is obvious that green GDP accounting has become increasingly popular. Green GDP can be calculated by various methods concerning energy intensity, pollution intensity [7], environmental costs [6] and ecosystem service value [9,12,13]. Until now, using different methods to measure green GDP, comparing differences between green GDP and GDP and mapping the spatial distribution of green GDP have been widely studied concerning the historical value of green GDP.…”

Section: Introductionmentioning

confidence: 99%

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National Green GDP Assessment and Prediction for China Based on a CA-Markov Land Use Simulation Model

Lin

et al. 2019

Sustainability

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Green Gross Domestic Product (GDP) is an important indicator to reflect the trade-off between the ecosystem and economic system. Substantial research has mapped historical green GDP spatially. But few studies have concerned future variations of green GDP. In this study, we have calculated and mapped the spatial distribution of the green GDP by summing the ecosystem service value (ESV) and GDP for China from 1990 to 2015. The pattern of land use change simulated by a CA-Markov model was used in the process of ESV prediction (with an average accuracy of 86%). On the other hand, based on the increasing trend of GDP during the period of 1990 to 2015, a regression model was built up to present time-series increases in GDP at prefecture-level cities, having an average value of R square (R2) of approximately 0.85 and significance level less than 0.05. The results indicated that (1) from 1990 to 2015, green GDP was increased, with a huge growth rate of 78%. Specifically, the ESV value was decreased slightly, while the GDP value was increased substantially. (2) Forecasted green GDP would increase by 194978.29 billion yuan in 2050. Specifically, the future ESV will decline, while the rapidly increased GDP leads to the final increase in future green GDP. (3) According to our results, the spatial differences in green GDP for regions became more significant from 1990 to 2050.

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Section: Conclusion and Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

National Green GDP Assessment and Prediction for China Based on a CA-Markov Land Use Simulation Model

Lin

et al. 2019

Sustainability

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“…Due to LULC changes and urbanization, most of these studies showed moderate to significant decrease in ESV [15,16,28,29,41,43] while others found almost no change [46]. Some studies have also revealed an increase in ESV [47][48][49][50]. Such variations in the services value are of the following reasons.…”

mentioning

confidence: 99%

Impact of land use land cover changes on ecosystem service value – A case study of Guangdong, Hong Kong, and Macao in South China

2020

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The rapid increase in anthropogenic activities, socioeconomic development, and land use land cover (LULC) changes since the opening of economic reforms (1978), have changed the ecosystem service value (ESV) in Guangdong, Hong Kong, and Macao (GKHM) region located in South China. This leads to the requirement of a significant tailored analysis of ecosystem services regarding incisive and relevant planning to ensure sustainability at regional level. This study focuses on the use of Landsat satellite imagery to quantify the precise impact of LULC changes on the ecosystem services in GHKM over the past three decades . The most renowned established unit value transfer method has been employed to calculate the ESV. The results show that the total ecosystem service value in GHKM has decreased from 680.23 billion CNY in 1986 to 668.45 billion CNY in 2017, mainly due to the decrease in farmland and fishponds. This overall decrease concealed the more dynamic and complex nature of the individual ESV. The most significant decrease took place in the values of water supply (-22.20 billion CNY, -14.72%), waste treatment (-20.77 billion CNY, −14.63%), and food production (-7.96 billion CNY, −33.18%). On the other hand, the value of fertile soil formation and retention (6.28 billion CNY, +7.26%) and recreation and culture (5.09 billion CNY, +12.91%) increased. Furthermore, total ESV and ESV per capita decreased significantly with the continuous increase in total gross domestic product (GDP) and GDP per capita. A substantial negative correlation exists between farmland ESV and GDP indicating human encroachment into a natural and semi natural ecosystems. The results suggest that in the rapidly urbanizing region, the protection of farmland and to control the intrusion of urban areas has marked an important societal demand and a challenge to the local government. This required a pressing need for smart LULC planning and to improve policies and regulation to guarantee ecosystem service sustainability for acceptable life quality in the study area and other fast expanding urban areas in China.

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“…However, Xie found that there is no linear relationship between the total ESV and GDP [5]. Li found that the ESV in cities of Shannxi Province increased slightly or remained stable with the growth of GDP [44]. However, all these studies did not control variables, which means that the relationship between ESVs and GDP also includes the impact of other socioeconomic factors on ESVs.…”

Section: Plos Onementioning

confidence: 98%

Deep learning: To better understand how human activities affect the value of ecosystem services—A case study of Nanjing

Liu

Wang

et al. 2020

PLoS ONE

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The value of ecosystem services is affected by increasing human activities. However, the anthropogenic driving mechanisms of ecosystem services are poorly understood. Here, we established a deep learning model to approximate the ecosystem service value (ESV) of Nanjing City using 23 socioeconomic factors. A multi-view analysis was then conducted on feasible impact mechanisms using model disassembly. The results indicated that certain factors had their own significant and independent effects on ESV, such as the proportion of water areas in the land-use structure and the output value of the secondary industry. The proportion of ecological water should be increased as much as possible, whereas the output value of the secondary industry should be reasonably controlled in Nanjing. Other intrinsically related factors were likely to be composited together to affect ESV, such as industrial water consumption and industrial electricity consumption. In Nanjing, simultaneously optimizing socioeconomic factors related to city size, resources, and energy use efficiency likely represents an effective management strategy for maintaining and enhancing regional ecological service capabilities. The results of this work suggest that deep learning is an effective method of deepening studies on the prediction of ESV trends and human-driven mechanisms.

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Spatial disparity dynamics of ecosystem service values and GDP in Shaanxi Province, China in the last 30 years

Cited by 15 publications

References 49 publications

National Green GDP Assessment and Prediction for China Based on a CA-Markov Land Use Simulation Model

National Green GDP Assessment and Prediction for China Based on a CA-Markov Land Use Simulation Model

Impact of land use land cover changes on ecosystem service value – A case study of Guangdong, Hong Kong, and Macao in South China

Deep learning: To better understand how human activities affect the value of ecosystem services—A case study of Nanjing

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