Mapping ecosystem services bundles to detect high- and low-value ecosystem services areas for land use management

Yang, Yanying; Zheng, Hua; Kong, Lingqiao; Huang, Binbin; Xu, Weihua; Ouyang, Zhiyun

doi:10.1016/j.jclepro.2019.03.242

Cited by 120 publications

(35 citation statements)

References 30 publications

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“…A key distinguishing feature of the LCA is that the mathematical protocol developed for measuring the elementary flows-materials and energy-is steady-state-based and uses a top-down causality model [35] rather than a constant flow rate (mass per unit time) [41]. Additionally, LCA accounts for flows but not natural infrastructure or ecological funds (EFs) that means that the inflows (LCI) and outflows (products and potential environmental impacts) of LCA do not take into account the EF´s availability (soil, aquatic, and terrestrial ecosystems, atmosphere, and ecological resources) to assess the PS [42]. This means that LCA does not consider reciprocal relationships between the EFs and their flows, i.e., its nexus.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Approach to Determining the Capacity of Ecosystems to Supply Ecosystem Services into Life Cycle Assessment for a Carbon Capture System

Morales-Mora¹,

Bravo²,

Baril³

et al. 2020

Applied Sciences

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In the life cycle assessment (LCA) method, it is not possible to carry out an integrated sustainability analysis because the quantification of the biophysical capacity of the ecosystems to supply ecosystem services is not taken into account. This paper considers a methodological proposal connecting the flow demand of a process or system product from the technosphere and the feasibility of the ecosystem to supply based on the sink capacity. The ecosystem metabolism as an analytical framework and data from a case study of an LCA of combined heat and power (CHP) plant with and without post-combustion carbon capture (PCC) technology in Mexico were applied. Three scenarios, including water and energy depletion and climate change impact, are presented to show the types of results obtained when the process effect of operation is scaled to one year. The impact of the water–energy–carbon nexus over the natural infrastructure or ecological fund in LCA is analyzed. Further, the feasibility of the biomass energy with carbon capture and storage (BECCS) from this result for Mexico is discussed. On the supply side, in the three different scenarios, the CHP plant requires between 323.4 and 516 ha to supply the required oil as stock flow and 46–134 ha to supply the required freshwater. On the sink side, 52–5,096,511 ha is necessary to sequester the total CO2 emissions. Overall, the CHP plant generates 1.9–28.8 MW/ha of electricity to fulfill its function. The CHP with PCC is the option with fewer ecosystem services required.

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Section: Introductionmentioning

confidence: 99%

An Integrated Approach to Determining the Capacity of Ecosystems to Supply Ecosystem Services into Life Cycle Assessment for a Carbon Capture System

Morales-Mora¹,

Bravo²,

Baril³

et al. 2020

Applied Sciences

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“…There are five major types of driving forces: socioeconomic, political, technological, natural and cultural driving forces [2].The factors that directly affect the ecosystem process are called direct driving forces, which can be measured and identified at different precisions; by changing the direct driving force, indirect effects are called indirect driving forces, which determine the magnitude of action from its impact on the direct driving force [3]. Exploring the underlying mechanisms can provide an important basis for land management in an environment of greater human impact, it also helps to understand the trade-offs and synergies among multiple environmental factors [4].…”

Section: Introductionmentioning

confidence: 99%

Influence of meteorological factors on ecosystem services value: a case study of Beijing-Tianjin-Hebei region, China

et al. 2020

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Meteorological factors are one of the natural factors, which affect ecosystem services value (ESV). Influence of meteorological factors was studied in Beijing-Tianjin-Hebei region using ordinary least square (OLS) with geographical weighted regression (GWR). The main aim of this study was to reveal the differences in the influence mechanism at the global and local levels. The main meteorological factors influencing ESV were temperature and precipitation, followed by humidity. Days with annual daily precipitation≥0.1mm, annual minimum precipitation and annual average relative humidity were three important meteorological factors. Annual temperature range, annual minimum precipitation, days with annual daily precipitation≥0.1mm, in particular, the last one had an obvious positive effect. The positive and negative effects of annual average relative humidity were coexisting, and the negative effect was the main. It was obvious that the spatial distribution characteristics of the local influence mechanism. The local model of GWR can better solve the spatial non-stationarity of the dependent and independent variables, thus it was better than the global model of OLS. The results also provide detailed field information on the different effects of meteorological factors at different locations.E3S Web of Conferences 158, 06003 (2020)

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“…Studies have begun to include the socioeconomic drivers of ESVs into consideration for the implementation of responsive policies. Yang used Pearson's correlations to examine the relationships among ecosystem services and found that ESVs are tightly correlated with socioeconomic status in the Beijing-Tianjin-Hebei Metropolitan Area [20]. Wu found nonlinear relations between GDP and ESV and a similar pattern between population density and ESV by employing analyses of variance (ANOVA), although a more complete causality was not explained [21].…”

Section: Introductionmentioning

confidence: 99%

“…In general, most existing studies on humandriven mechanisms of socio-economic factors on ESVs focus on the traditional phase and only conduct qualitative descriptions or extract relatively important socioeconomic factors using statistical analysis methods, such as correlation analysis, regression analysis, GeoDetector, PCA, etc. [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

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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Mapping ecosystem services bundles to detect high- and low-value ecosystem services areas for land use management

Cited by 120 publications

References 30 publications

An Integrated Approach to Determining the Capacity of Ecosystems to Supply Ecosystem Services into Life Cycle Assessment for a Carbon Capture System

An Integrated Approach to Determining the Capacity of Ecosystems to Supply Ecosystem Services into Life Cycle Assessment for a Carbon Capture System

Influence of meteorological factors on ecosystem services value: a case study of Beijing-Tianjin-Hebei region, China

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

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