Journal of Resources and Ecology ›› 2020, Vol. 11 ›› Issue (5): 499-507.DOI: 10.5814/j.issn.1674-764x.2020.05.007
• Human Activities and Ecosystem • Previous Articles Next Articles
WANG Yajun, ZHONG Lifang
Received:2019-11-03
Accepted:2020-02-28
Online:2020-09-30
Published:2020-09-30
About author:WANG Yajun, E-mail: wangyajun892@126.com
Supported by:WANG Yajun, ZHONG Lifang. Research Framework for Ecosystem Vulnerability: Measurement, Prediction, and Risk Assessment[J]. Journal of Resources and Ecology, 2020, 11(5): 499-507.
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URL: http://www.jorae.cn/EN/10.5814/j.issn.1674-764x.2020.05.007
| Target layer | Index layer | Research object | Literature sources |
|---|---|---|---|
| Exposure, sensitivity, adaptability | Population pressure, environmental degradation, industrial pollution, income difference, natural background, economic strength, infrastructure, fiscal expenditure, social security | Loess plateau area | |
| Ecological environment, natural resources, social economy, sustainability | Vegetation index, soil organic matter, rainfall, water resources, engel coefficient, gross domestic product(GDP) index, natural population growth rate, contribution rate of tertiary industry | Hilly mining area | |
| Pressure, sensitivity, stability | Population pressure, social pressure, environmental pressure, economic pressure, desertification sensitivity, salinity sensitivity, function, vitality, elasticity, structural limitations | Turpan area | |
| Natural quality, anthropogenic pressure | Precipitation rate, drought index, soil depth, soil parent material, vegetation coverage, population growth rate, population density | Italy | |
| Causes and results | Land use type, annual average precipitation, temperature, humidity, population density, per capita income, cultivated land area | Qinghai-Tibet region | |
| Ecological pressure, ecological sensitivity, ecological resilience | Inverse fractal dimension, disturbance index, terrain index, soil sensitivity index, dominance, fragmentation | Wugong mountain |
Table 1 Literature review of EV measurement and prediction indicators
| Target layer | Index layer | Research object | Literature sources |
|---|---|---|---|
| Exposure, sensitivity, adaptability | Population pressure, environmental degradation, industrial pollution, income difference, natural background, economic strength, infrastructure, fiscal expenditure, social security | Loess plateau area | |
| Ecological environment, natural resources, social economy, sustainability | Vegetation index, soil organic matter, rainfall, water resources, engel coefficient, gross domestic product(GDP) index, natural population growth rate, contribution rate of tertiary industry | Hilly mining area | |
| Pressure, sensitivity, stability | Population pressure, social pressure, environmental pressure, economic pressure, desertification sensitivity, salinity sensitivity, function, vitality, elasticity, structural limitations | Turpan area | |
| Natural quality, anthropogenic pressure | Precipitation rate, drought index, soil depth, soil parent material, vegetation coverage, population growth rate, population density | Italy | |
| Causes and results | Land use type, annual average precipitation, temperature, humidity, population density, per capita income, cultivated land area | Qinghai-Tibet region | |
| Ecological pressure, ecological sensitivity, ecological resilience | Inverse fractal dimension, disturbance index, terrain index, soil sensitivity index, dominance, fragmentation | Wugong mountain |
| Primary index | Secondary index | Tertiary index |
|---|---|---|
| Indicators of Natural factor | Terrain, climate, soil, vegetation, geology, water resources | Vegetation coverage rate, terrain distribution, precipitation, soil type, soil erosion rate, total water resources |
| Indicators of social factors | Social development index | Population density, natural population growth rate, per capita arable land area, urbanization level, poverty rate, unemployment rate, school enrollment rate |
| Indicators of economic factors | Economic development index | Per capita GDP, proportion of primary industry, proportion of secondary industry, per capita net income of farmers, consumer price index, industrial wastewater discharge, energy consumption |
Table 2 The EV index system
| Primary index | Secondary index | Tertiary index |
|---|---|---|
| Indicators of Natural factor | Terrain, climate, soil, vegetation, geology, water resources | Vegetation coverage rate, terrain distribution, precipitation, soil type, soil erosion rate, total water resources |
| Indicators of social factors | Social development index | Population density, natural population growth rate, per capita arable land area, urbanization level, poverty rate, unemployment rate, school enrollment rate |
| Indicators of economic factors | Economic development index | Per capita GDP, proportion of primary industry, proportion of secondary industry, per capita net income of farmers, consumer price index, industrial wastewater discharge, energy consumption |
| Measurement model | Model content | Model evaluation | Scope of application |
|---|---|---|---|
| PSR (pressure- state-response) model | Pressure indicators based on the effects of human economic and social activities on the environment, the status quo of ecosystem and natural environment represented by the status indicators, and the response indicators are established to prevent the negative impacts of human activities on the environment | Three basic questions “what happened, why did it happen and how to do it” are answered, which fully explain the situation of the evaluation object compared with the reference standard | Applicable to regional environment, soil and water resources and agricultural protection |
| VSD (exposure-sensitive- adaptation) model | Vulnerability is studied from three dimensions: exposure degree, sensitivity and adaptive potential. Each indicator is refined with circle-level data, and evaluated effectively and clearly by “aspect layer—index layer—parameter layer” | It fails to clarify which reflects the natural factors, and which reflects the human factors | Suitable for the basic data of comprehensive regional EV measurement |
| Pressure sensitivity resilience model | The intensity of ecological pressure includes area-weighted average fractal dimension reciprocal and disturbance indexes. The ecological sensitivity includes soil erosion sensitivity index, terrain index and landscape fragmentation index. The ecological resilience refers to the self-resilience of an ecosystem and when it is disturbed, it is related to the stability of its internal organizational structure | The emphasis is put on the natural factors, and the proportion of human factors in the index is not high | The vulnerability of ecologically fragile areas are measured and compared |
| Fuzzy evaluation method | Establish the index system and weight, calculate factors for the membership of each evaluation index vector, evaluate regional vulnerability degree | Fuzzy trigonometric functions can reduce the shortcomings of subjective effects, which has certain objectivity, but the index of significance is not obvious, and is a heavy workload | Suitable for a specific areas or multiple regions |
| Analytic hierarchy process (AHP) | Establish the evaluation index, score and weight the index, multiplied by the score value and weight, which are added up to obtain the total score to determine the degree of ecological vulnerability | It provides a clearer idea and logic for selection of related indices. The index selection is subjective | Suitable for the analysis of regional and internal evaluation units |
| Principal component method | Data standardization, set up the correlation coefficient matrix, calculate eigenvalues and eigenvectors and cumulative contribution rates, and obtain the main ingredients of vulnerability analysis | Variable selection of dimensions is not restricted, but it particularly focuses on the main ingredients, which causes some information to be missed, and fails to fully reflect the index of all information | Suitable for regional analysis of the internal evaluation unit |
| BP neural network method | Set the objective function of the calculated index, and the weight between the input and output layers of the index can be adjusted and modified with the gradient descent method | Intervention processing, compatibility | Deals with the measurement of regional ecological vulnerability with some complex states |
| Cause-result evaluation method | Establish the corresponding index system according to the characteristics and causes of EV, and the entropy weight method is usually used to assign the weight to each index | Relatively simple, and difficult to deal with complex state | Used for the comparison of vulnerability degrees between regions for a rough analysis |
| Set pair analysis | Establish coefficient of difference degree and correlation degree, weights set, scheme set and evaluation set, the standard deviation classification method is used to measure and classify the EV | The calculation is complicated, and the analysis result has some intuitiveness | Can be used to measure and analyze the EV of regional units |
| Landscape ecology model | Computer simulation data is used to characterize the dynamic characteristics of EV, which is combined with GIS, remote sensing data and other system analysis data | This model focuses on local spatial analysis and ignores the influence of human factors | Analysis of EV from the perspectives of regional space and spatial heterogeneity |
| Grey relational degree | The reference sequence of ecological vulnerability characterization and the comparative sequence of influencing system behavior are determined, the data are processed dimensionless, the grey correlation coefficients of the reference sequence and the comparative sequence are calculated, and the correlation degree is sorted | The degree of correlation between vulnerability factors is emphasized | Used for comparative analysis between regions |
| Matter-element extension model | The classical domain, node domain and object element to be evaluated are determined, the index weight is set, the correlation degree is calculated, and finally the vulnerability degree of the member to be evaluated is obtained | This method is suitable for multi-factor analysis, which uses formal language to deal with the characteristics of ecological vulnerability | Used for delicate analysis of fragility between regions |
Table 3 Brief evaluation of EV measurement models
| Measurement model | Model content | Model evaluation | Scope of application |
|---|---|---|---|
| PSR (pressure- state-response) model | Pressure indicators based on the effects of human economic and social activities on the environment, the status quo of ecosystem and natural environment represented by the status indicators, and the response indicators are established to prevent the negative impacts of human activities on the environment | Three basic questions “what happened, why did it happen and how to do it” are answered, which fully explain the situation of the evaluation object compared with the reference standard | Applicable to regional environment, soil and water resources and agricultural protection |
| VSD (exposure-sensitive- adaptation) model | Vulnerability is studied from three dimensions: exposure degree, sensitivity and adaptive potential. Each indicator is refined with circle-level data, and evaluated effectively and clearly by “aspect layer—index layer—parameter layer” | It fails to clarify which reflects the natural factors, and which reflects the human factors | Suitable for the basic data of comprehensive regional EV measurement |
| Pressure sensitivity resilience model | The intensity of ecological pressure includes area-weighted average fractal dimension reciprocal and disturbance indexes. The ecological sensitivity includes soil erosion sensitivity index, terrain index and landscape fragmentation index. The ecological resilience refers to the self-resilience of an ecosystem and when it is disturbed, it is related to the stability of its internal organizational structure | The emphasis is put on the natural factors, and the proportion of human factors in the index is not high | The vulnerability of ecologically fragile areas are measured and compared |
| Fuzzy evaluation method | Establish the index system and weight, calculate factors for the membership of each evaluation index vector, evaluate regional vulnerability degree | Fuzzy trigonometric functions can reduce the shortcomings of subjective effects, which has certain objectivity, but the index of significance is not obvious, and is a heavy workload | Suitable for a specific areas or multiple regions |
| Analytic hierarchy process (AHP) | Establish the evaluation index, score and weight the index, multiplied by the score value and weight, which are added up to obtain the total score to determine the degree of ecological vulnerability | It provides a clearer idea and logic for selection of related indices. The index selection is subjective | Suitable for the analysis of regional and internal evaluation units |
| Principal component method | Data standardization, set up the correlation coefficient matrix, calculate eigenvalues and eigenvectors and cumulative contribution rates, and obtain the main ingredients of vulnerability analysis | Variable selection of dimensions is not restricted, but it particularly focuses on the main ingredients, which causes some information to be missed, and fails to fully reflect the index of all information | Suitable for regional analysis of the internal evaluation unit |
| BP neural network method | Set the objective function of the calculated index, and the weight between the input and output layers of the index can be adjusted and modified with the gradient descent method | Intervention processing, compatibility | Deals with the measurement of regional ecological vulnerability with some complex states |
| Cause-result evaluation method | Establish the corresponding index system according to the characteristics and causes of EV, and the entropy weight method is usually used to assign the weight to each index | Relatively simple, and difficult to deal with complex state | Used for the comparison of vulnerability degrees between regions for a rough analysis |
| Set pair analysis | Establish coefficient of difference degree and correlation degree, weights set, scheme set and evaluation set, the standard deviation classification method is used to measure and classify the EV | The calculation is complicated, and the analysis result has some intuitiveness | Can be used to measure and analyze the EV of regional units |
| Landscape ecology model | Computer simulation data is used to characterize the dynamic characteristics of EV, which is combined with GIS, remote sensing data and other system analysis data | This model focuses on local spatial analysis and ignores the influence of human factors | Analysis of EV from the perspectives of regional space and spatial heterogeneity |
| Grey relational degree | The reference sequence of ecological vulnerability characterization and the comparative sequence of influencing system behavior are determined, the data are processed dimensionless, the grey correlation coefficients of the reference sequence and the comparative sequence are calculated, and the correlation degree is sorted | The degree of correlation between vulnerability factors is emphasized | Used for comparative analysis between regions |
| Matter-element extension model | The classical domain, node domain and object element to be evaluated are determined, the index weight is set, the correlation degree is calculated, and finally the vulnerability degree of the member to be evaluated is obtained | This method is suitable for multi-factor analysis, which uses formal language to deal with the characteristics of ecological vulnerability | Used for delicate analysis of fragility between regions |
| Qualitative prediction | Quantitative prediction | ||
|---|---|---|---|
| Method | Evaluation | Method | Evaluation |
| Deduction method | Based on the past and current data, the future trends of EV can be deduced. At the macro level, the prediction has good applicability in the case of low precision and missing data | Life zone model | Select a specific model according to the vulnerability data, input parameters and constraints, and simulate the evolutionary trend of the ecological vulnerability. It has the limitation of a high requirement of data quality, which requires strict discrimination, otherwise significant errors may exist in trend judgment |
| Scenario analysis model | It can be used for scenario prediction of greenhouse gas emission and concentrations, and prediction of the change trend of climate vulnerability. It has the limitation that it can only conduct simulations and predictions based on the natural ecosystem, and rarely involves the social and economic ecosystems | ||
| Logistic regression method | According to the law of succession, the trend characteristics of data are used for prediction. It has the shortcoming of excessive dependency on the subjective evaluation of the principles, which does not have a high requirement for the quality and prediction of data | ||
| Neural network model | With certain accuracy, it can efficiently process noisy and incomplete data and non-linear complex systems. However, this model does not have strong interpretability for simple ecosystems | ||
Table 4 Prediction methods and evaluation of EV
| Qualitative prediction | Quantitative prediction | ||
|---|---|---|---|
| Method | Evaluation | Method | Evaluation |
| Deduction method | Based on the past and current data, the future trends of EV can be deduced. At the macro level, the prediction has good applicability in the case of low precision and missing data | Life zone model | Select a specific model according to the vulnerability data, input parameters and constraints, and simulate the evolutionary trend of the ecological vulnerability. It has the limitation of a high requirement of data quality, which requires strict discrimination, otherwise significant errors may exist in trend judgment |
| Scenario analysis model | It can be used for scenario prediction of greenhouse gas emission and concentrations, and prediction of the change trend of climate vulnerability. It has the limitation that it can only conduct simulations and predictions based on the natural ecosystem, and rarely involves the social and economic ecosystems | ||
| Logistic regression method | According to the law of succession, the trend characteristics of data are used for prediction. It has the shortcoming of excessive dependency on the subjective evaluation of the principles, which does not have a high requirement for the quality and prediction of data | ||
| Neural network model | With certain accuracy, it can efficiently process noisy and incomplete data and non-linear complex systems. However, this model does not have strong interpretability for simple ecosystems | ||
| Method | Content and evaluation |
|---|---|
| Risk assessment index method | Based on the subjective evaluation method, the severity and possibility of EV are determined according to the evaluation purpose, and an expert questionnaire is compiled for scoring, so as to determine the possibility and risk level of adverse effects. It has certain subjectivity, which is applicable to fragile ecosystems affected by both human and natural factors. The model has the limitation that the subjective factor is too strong, so it cannot be evaluated in an objective and effective way |
| Risk synthesis index method | The relationship between the landscape structure and the regional EV risk is established, and the vulnerability risk index is determined by the area proportions of landscape components and landscape loss index. It is mainly used to analyze complex ecosystems that are more strongly influenced by natural factors than by human factors. The shortcoming is that it can only analyze simple ecosystems under the influence of natural factors, while it does not work for complicated ecosystems with less interference of human factors |
| Risk causal chain model | Three-dimensional models are established by identifying the risk receptors, exposure - response processes and ecological endpoints. Risk = risk probability ×vulnerability ×degree of loss. It includes the explicit characterization of the system exposure response process, the sensitivity of loss as a loss correction factor, and a more comprehensive reflection of the vulnerability pattern of the ecosystem. The main limitation is that it is too objective, and so it lacks a certain subjective judgment |
| “Probability-loss” two-dimensional model | Determines the probability and consequences of ecological vulnerability events. Risk = probability of risk × outcome. The method is simple, but it does not consider whether the ecosystem is affected by the risk source or its sensitivity |
Table 5 Risk assessment methods and evaluation of EV
| Method | Content and evaluation |
|---|---|
| Risk assessment index method | Based on the subjective evaluation method, the severity and possibility of EV are determined according to the evaluation purpose, and an expert questionnaire is compiled for scoring, so as to determine the possibility and risk level of adverse effects. It has certain subjectivity, which is applicable to fragile ecosystems affected by both human and natural factors. The model has the limitation that the subjective factor is too strong, so it cannot be evaluated in an objective and effective way |
| Risk synthesis index method | The relationship between the landscape structure and the regional EV risk is established, and the vulnerability risk index is determined by the area proportions of landscape components and landscape loss index. It is mainly used to analyze complex ecosystems that are more strongly influenced by natural factors than by human factors. The shortcoming is that it can only analyze simple ecosystems under the influence of natural factors, while it does not work for complicated ecosystems with less interference of human factors |
| Risk causal chain model | Three-dimensional models are established by identifying the risk receptors, exposure - response processes and ecological endpoints. Risk = risk probability ×vulnerability ×degree of loss. It includes the explicit characterization of the system exposure response process, the sensitivity of loss as a loss correction factor, and a more comprehensive reflection of the vulnerability pattern of the ecosystem. The main limitation is that it is too objective, and so it lacks a certain subjective judgment |
| “Probability-loss” two-dimensional model | Determines the probability and consequences of ecological vulnerability events. Risk = probability of risk × outcome. The method is simple, but it does not consider whether the ecosystem is affected by the risk source or its sensitivity |
Fig. 1 Logical framework of EV research system
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