Industrial and ecological system interaction relationship quantification method considering spatial adjacency relationship

Abstract

The invention discloses an industry and ecosystem interaction relationship quantification method considering a spatial adjacency relationship, which is based on an ecosystem service theory system, identifies an interaction influence mechanism of an industry and an ecosystem under a spatial adjacency perspective on the basis of clarification of a complex interaction relationship of the industry and the ecosystem in a geographic space, and determines an interaction relationship between the industry and the ecosystem. And on the basis of a spatial simultaneous equation modeling technology, an industry and ecosystem interaction relationship quantitative model is constructed. According to the method, the influence disturbance of an adjacent observation point area on observation points is fully considered, the coordination, tradeoff and causal relationship among ecosystem services, industries and driving factors thereof are incorporated into the same metering analysis system, a feedback loop relationship is established, and the influence intensity of the industries on the ecosystem in geographic space is systematically and comprehensively identified. According to the method, a new model means is provided for research on the complex relationship between the industry and the ecological system, and important theoretical and method support is provided for high-quality development of social economy and implementation of an ecological protection strategy.

Description

Technical Field

[0001] The present invention relates to the field of ecological environmental technology, and is particularly applicable to a method for quantifying the interaction between industries and ecosystems by considering spatial adjacency. Background Art

[0002] Ecosystem services are the direct or indirect benefits provided by natural ecosystems to humans. They link the socio-economic system and the ecosystem, and can be summarized as the material cycle, energy flow, and information transmission between the biological and non-biological environments within a specific time and space. With the continuous intensification of human production activities, the stability of the supply of ecosystem services has been seriously affected, leading to a fierce trade-off between economic and social development and ecological protection. As an important carrier of human economic activities, the level of development of industry is the core driving force of economic growth. Different industrial structure characteristics are also regarded as an important symbol to distinguish the development level of a country or region, and are also one of the important factors that determine the regional economic resilience. Scientifically identifying the interaction between industry and ecosystem is an important step to break the trade-off model between economic and social development and ecological protection. Figure 1 As shown, a diagram of the interaction mechanism between industry and ecosystem services is presented.

[0003] For example, industrial development directly leads to significant changes in land use patterns, primarily in terms of land use type, intensity, pattern, and efficiency. Industrial development can also indirectly impact ecosystem provisioning capacity by altering climate conditions and landscape patterns. Understanding the coupling relationship between industry and ecosystem services can help managers formulate more scientific management strategies to achieve coordinated development of industry and ecosystems.

[0004] Existing research on the relationship between industry and ecosystem services falls into three main categories: The first is comprehensive indicator analysis. This method selects several economic and ecological indicators to construct a comprehensive evaluation index system to assess the impact of industrial activities on the ecosystem. The research results can provide a preliminary assessment of the impact of specific industrial development on the ecology. This method is suitable for research with good data availability. The second is spatial analysis. This method analyzes the spatial distribution pattern of industries, extracts the common laws of their spatial existence, and then proposes spatial zoning management strategies. This method is suitable for research with strong spatial heterogeneity of variables. The third is causal analysis. This method evaluates the unidirectional driving relationship between industry and individual ecosystem services by establishing a regression model, thereby deepening the understanding of how specific ecosystem services respond to changes in individual industries. This method is suitable for studying continuous time series data.

[0005] The above three types of research not only oversimplify the complex relationship between industry and ecosystem services, but also rely on their own mandatory assumptions, which makes them lack objectivity. Therefore, this paper proposes a method to quantify the complex interactions between industry and ecosystem services, while fully considering the spatial proximity of industries. Summary of the Invention

[0006] The purpose of the present invention is to provide a method for quantifying the interaction between industry and ecosystems that takes into account spatial adjacency, which is used to study the complex interactions between land, weather, vegetation, landscape, industry and ecosystem service variables.

[0007] To achieve the above object, the present invention adopts the following technical solutions: The method for quantifying the interaction between industries and ecosystems taking into account spatial adjacency relationships of the present invention comprises the following steps: S1, rasterize the study area and collect data for each grid in time series, including land data, meteorological data, vegetation data, landscape data, and industrial data; S2, unify the data to the same precision, use the InVEST model to calculate the quality of ecosystem services in the smallest research unit, and calibrate the calculation results; S3, determining the spatial adjacency relationship between the minimum research units, performing ESDA analysis on the calculation results, and determining the spatial correlation of ecosystem services between the minimum research units; In step S4, based on the spatial simultaneous equation modeling technology, the explanatory variables and explained variables are determined, and based on the data collected in step S1, a set of spatial simultaneous equations for the interaction between industry and ecosystem is constructed and solved.

[0008] Furthermore, in step S1, the land data include land use and land cover data, digital elevation, root restricting layer depth, and soil erosion index; the meteorological data include rainfall, evapotranspiration, and rainfall erosion index; the vegetation data include root restricting layer depth, plant available water content, and vegetation cover normalization index; the landscape data are calculated from the land use and land cover data, including NP (number of patches), PARA (average perimeter-area ratio), SHDI (Shannon diversity index), and AI (aggregation index); the industry data include the output value of the research industry.

[0009] Furthermore, the data in step S2 are unified from high precision to low precision and are complete at the boundaries of each grid; and the ecosystem services calculated within each minimum research unit include at least two types.

[0010] Furthermore, the spatial adjacency relationship in step S3 includes edge adjacency, corner adjacency, distance adjacency, and inverse distance adjacency; the determined spatial adjacency relationship is determined based on the vector topological shape of the minimum research unit in the research area.

[0011] Furthermore, in step S3, the ESDA analysis is to calculate the Moran index of the quality of ecosystem services between the smallest research units to determine whether the ecosystem services between the smallest research units are positively spatially correlated, negatively spatially correlated, or randomly distributed.

[0012] Furthermore, the spatial correlation determined in step S3 is verified by Monte Carlo simulation; when the Monte Carlo simulation verification result is consistent with the spatial correlation result determined in step S3, step S4 is executed.

[0013] Furthermore, the explained variable in step S4 is ecosystem service; and the explanatory variable is the influencing factor of the interaction between industry and ecosystem.

[0014] Furthermore, when solving the spatial simultaneous equations of the interaction between the industry and the ecosystem, the generalized three-stage spatial least squares estimation method is used to estimate the parameters of the spatial simultaneous equations.

[0015] Furthermore, the impact factors are selected and determined from the data collected in step S1, and the impact factors are subject to a multicollinearity test.

[0016] Furthermore, the Berndt, McElroy, and Judge criteria were used to evaluate the fitting performance of the solutions to the spatial simultaneous equations of the interaction between the industry and the ecosystem.

[0017] The advantage of the present invention is that it is based on the theoretical system of ecosystem services, and on the basis of clarifying the complex interactive relationship between industry and ecosystem in geographic space, it identifies the interactive impact mechanism between industry and ecosystem from the perspective of spatial proximity, and constructs a quantitative model of the interactive relationship between industry and ecosystem based on the spatial simultaneous equation modeling technology. It fully considers the impact disturbance of the adjacent areas of the observation point on the observation point, and at the same time incorporates the synergy, trade-off, and causal relationship between ecosystem services, industry and its driving factors into the same quantitative analysis system, establishes a feedback loop relationship, and systematically and comprehensively identifies the intensity of the impact of industry on the ecosystem in geographic space. The present invention provides a new modeling method for the study of the complex relationship between industry and ecosystem, and provides important theoretical and methodological support for the implementation of high-quality social and economic development and ecological protection strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 Schematic diagram of the interaction mechanism between industry and ecosystem services.

[0019] Figure 2 The present invention is a flowchart of the method.

[0020] Figure 3 This is an illustration of the research area in Example 2 of the present invention.

[0021] Figure 4 This is a data sample collected in Example 2 of the present invention.

[0022] Figure 5 This is a spatial display of the calculation results of the ecosystem service quality in Example 2 of the present invention.

[0023] Figure 6 This is an illustration of the spatial neighbor relationship in Example 2 of the present invention.

[0024] Figure 7 Calculation results of the degree of spatial correlation in Example 2 of the present invention are displayed.

[0025] Figure 8 The solution results of the simultaneous equations in Example 2 of the present invention are displayed.

[0026] Figure 9 The results show the fitting performance of the simultaneous equations in Example 2 of the present invention.

[0027] Figure 10 This is a relationship diagram of the industry and ecosystem service feedback loop obtained in Example 2 of the present invention. DETAILED DESCRIPTION

[0028] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts are within the scope of protection of the present invention.

[0029] Example 1 The method for quantifying the interaction between industries and ecosystems considering spatial adjacency in the present invention is as follows: Figure 2 As shown, the following steps are included: S1, rasterize the study area and collect data for each grid in time series, including land data, meteorological data, vegetation data, landscape data, and industrial data.

[0030] Land data includes land use and land cover data, digital elevation, root-restricting layer depth, and soil erosion index. Meteorological data includes rainfall, evapotranspiration, and the rainfall erosion index. Vegetation data includes root-restricting layer depth, plant available water content, and the normalized vegetation cover index. Landscape data, calculated from land use and land cover data, include the number of NP patches, the average perimeter-to-area ratio (PARA), the Shannon diversity index (SHDI), and the AI ​​aggregation index; these indices were calculated using Fragstats software.

[0031] The industry data includes the output value of the industry.

[0032] S2, unify the data from high precision to low precision to the same precision, and ensure the integrity of the data at each grid boundary and continuity in the time series, and establish the attribution relationship between the data and each minimum research unit.

[0033] In research on the interaction between industry and ecosystems, the smallest research unit is generally defined by administrative district. For example, when studying the interaction between industry and ecosystems within a multi-city region, the smallest research unit is the area encompassing each city. When studying the interaction between industry and ecosystems across districts and counties within a city, the smallest research unit is the area encompassed by each district and county. Other criteria can also be used to determine the smallest research unit.

[0034] Based on step 1, the entire study area is divided into several grids. After collecting and organizing data according to the grid areas, it is necessary to use GIS software to determine the grids contained in each minimum study unit, and then count the data within these grids to the corresponding minimum study unit. If a grid spans multiple minimum study units, the grid is considered to belong to the grid based on the proportion of the overlapping area between the grid and the multiple minimum study units.

[0035] The InVEST model is then used to calculate the quantity of ecosystem services within each minimum study unit and calibrate the calculated results. At least two ecosystem services should be included in the calculation for each minimum study unit. The ecosystem services selected should be based on the characteristics of the study area.

[0036] S3. Determine the spatial adjacency between the smallest research units, perform ESDA analysis on the calculation results, and determine the spatial correlation between ecosystem services between the smallest research units. The spatial adjacency relationship includes edge adjacency, corner adjacency, distance adjacency, and inverse distance adjacency. The spatial adjacency relationship must be determined based on the vector topological shape of the smallest research unit in the study area. The mathematical expression of the spatial adjacency relationship is: ; ; in, The smallest research unit where industry and ESs (Ecosystem Services) coexist and The adjacent relationship expression, and If the space is adjacent, the value is 1, otherwise it is 0. Selecting different adjacent rules will result in and Have different adjacent expressions.

[0037] The ESDA analysis is to calculate the Moran's index (Moran's I) of the ecosystem service quality between the smallest research units and determine whether the ecosystem services between the smallest research units are positively spatially correlated, negatively spatially correlated, or randomly distributed.

[0038] The specific calculation formula of Moran's I is as follows: ; in, , . The smallest research unit the quality of ecosystem services provided; is the minimum total number of study units in the study area; The mean value of ecosystem services; It is the aggregation of the neighboring relationships of all the smallest research units; It is the Moran index of ecosystem services in the study area.

[0039] Then calculate the Moran index Score , the formula is as follows: ; and They are The expectation and variance of . The value range of is [-1,1], The closer the value is to 1 and When , it means that the ecosystem services have a stronger spatial positive correlation in the entire study area, that is, high-value clustering or low-value clustering; The closer the value is to -1 and When , it indicates that the spatial negative correlation of ecosystem services in the entire study area is stronger, that is, high values ​​are adjacent to low values; Value and The closer it is to 0, the more it means that the ecosystem services have no obvious spatial distribution characteristics in the entire study area, that is, they present a random distribution pattern.

[0040] In one embodiment, the spatial correlation determined in the step S3 needs to be verified through Monte Carlo simulation; when the Monte Carlo simulation verification result is consistent with the spatial correlation result determined in step S3, step S4 is executed.

[0041] In step S4, using spatial simultaneous equation modeling techniques, the explanatory and explained variables are determined, and a system of spatial simultaneous equations for the interactions between industries and ecosystems is constructed and solved. This process aims to clarify the complex interactions between industries and ecosystems in the study area and to identify the explanatory and explained variables within the study variables. The explained variables are ecosystem services, and the explanatory variables are the influencing factors of the interactions between industries and ecosystems, including changes in land use, weather, vegetation, and landscape. These influencing factors are selected from the data collected in step S1 and are tested for multicollinearity.

[0042] The spatial simultaneous equations for the interaction between industry and ecosystem are constructed. The basic form of the model is as follows: ; ; ; in, For the The explained variable in the equation is for The spatial hysteresis factor, is the first equation explanatory variables, For the The error term in the equation is 、 、 is the corresponding coefficient matrix.

[0043] Based on the data collected in step S1, when solving the spatial simultaneous equations of the interaction between the industry and the ecosystem, the generalized spatial three-stage least squares estimation method (GS3SLS) can be used to estimate the parameters of the spatial simultaneous equations.

[0044] The Berndt, McElroy, and Judge criteria can be used to measure the fitting performance of the spatial simultaneous equations for the interactions between industries and ecosystems. This method fully considers the potential spatial correlation of endogenous variables and the possible correlations between random perturbations in each equation, improving the validity of the estimation results.

[0045] Example 2 For the Loess Plateau region in the middle and upper reaches of the Yellow River Basin, taking cities as the smallest research units, the quantitative method of the interaction between industries and ecosystems considering spatial adjacency described in the present invention is used to study the interaction between industries and ecosystems among cities in the Loess Plateau region in the middle and upper reaches of the Yellow River Basin.

[0046] like Figure 3 Shown is the spatial location of the Loess Plateau. Figure 3 a is the spatial distribution of elevation of the Loess Plateau, Figure 3 b represents the administrative boundary of the cities involved in the Loess Plateau. The entire study area was rasterized, and data for the study area were collected in a time series, including land use / land cover, digital elevation, rainfall, evapotranspiration, root-restricting layer depth, plant available water content, vector boundary layer, rainfall erosion index, soil erosion index, and biophysical table.

[0047] The accuracy of all grid-scale data was unified. Combining the literature and the regional area of ​​the Loess Plateau, the present invention finally used the ArcGIS platform to sample all data in a 500 m × 500 m grid. The collection time scale was: 2000–2019 annual data. The data sample was as follows: Figure 4 As shown. Among them, Figure 4 a Sample land use / land cover data, Figure 4 b is a sample of digital elevation data. Figure 4 c is a rainfall data sample, Figure 4 d is a sample of surface evapotranspiration data, Figure 4 e is a sample of root limit depth data, Figure 4 f is the sample data of available water for plants, Figure 4 g is a sample of rainfall erosion index data, Figure 4 h is a sample of soil erosion data. Then, based on the characteristics of the Loess Plateau, representative ecosystem services are selected for calculation. This example selects water production ecosystem services (WY) and sediment output ecosystem services (SE), and uses the InVEST model to calculate the amount of ecosystem services within the minimum study area. The calculation results are displayed spatially as follows: Figure 5 shown. Figure 5 Figures a-1, a-2, and a-3 show the spatial display of the calculation results of WY mass in the Loess Plateau region in the middle and upper reaches of the Yellow River Basin in 2000, 2010, and 2019, respectively. Figures b-1, b-2, and b-3 show the spatial display of the calculation results of SE mass in the Loess Plateau region in the middle and upper reaches of the Yellow River Basin in 2000, 2010, and 2019, respectively.

[0048] Then, step 3 of the present invention is executed to apply the calculation results of the Loess Plateau ecosystem service grid to all cities involved in the Loess Plateau in the form of zonal statistics, and select the Queen space adjacent rule of the corners based on the Moran's index (Moran's I). The specific adjacent relationship is shown as follows: Figure 6As shown in , if two or more minimum research units are adjacent in the same row, column, or diagonal line, then the two or more minimum research units are considered adjacent. Calculate the spatial correlation of ecosystem services between cities on the Loess Plateau. The calculation results from 2000 to 2019 are as follows: Figure 7 As shown. Among them, Figure 7 a is the WY value (left vertical axis) and the Moran's I value of WY (right vertical axis), Figure 7 b is the SE value (left vertical axis) and the Moran's I value of SE (right vertical axis), Figure 7 c is the 999 Monte Carlo simulations of WY’s Moran’s I value, Figure 7 d is the 999 Monte Carlo simulations of Moran's I value of SE.

[0049] from Figure 7 As can be seen from the data, the Moran Index of ecosystem services across the Loess Plateau remained high from 2000 to 2019, indicating significant spatial correlation between WY and SE across the Loess Plateau. The spatial correlation of WY was more pronounced than that of SE. Between 2000 and 2019, with the exception of 2005-2010, the absolute quantities of WY and SE exhibited a certain degree of synergy. The spatial correlation of WY's absolute quantity also exhibited a synergistic trend, with the exception of 2005-2010, suggesting that the spatial concentration of WY facilitates its service provision. However, the absolute quantity and spatial correlation of SE exhibited a trade-off, with the exception of 2005-2010, suggesting that the spatial concentration of SE inhibits its service provision.

[0050] To ensure the reliability of the research conclusions, the inventors used 2019 data as an example and assumed that there was no spatial correlation between WY and SE (assuming spatial correlation was 0). They then randomly permuted the spatial positions of the original WY and SE data 999 times through Monte-Carlo (MC) simulations and calculated the global Moran's index for each permutation to verify the conclusions. The results showed that the global Moran's index for WY and SE showed a roughly normal distribution across 999 MC simulations, and the probability of the global Moran's index obtained from the actual calculated WY and SE data was extremely small, so the null hypothesis could be rejected. This further demonstrates the existence of significant spatial effects of ecosystem services on the Loess Plateau, paving the way for the subsequent relationship quantification step.

[0051] Considering data reliability and availability, this example uses primary industry value added (PGDP) and secondary industry value added (SGDP) to represent the level of regional economic development. Annual average rainfall (PRE) and annual average evapotranspiration (PET) are selected as key factors for measuring the climate conditions in the study area. The Normalized Difference Vegetation Index (NDVI) is also an effective indicator for measuring crop and vegetation cultivation in the study area. In addition, five landscape indices were selected to comprehensively reflect human land use in the study area: number of patches (NP), average perimeter-to-area ratio (PARA), Shannon diversity index (SHDI), and aggregation index (AI). These indices serve as influencing factors for the interaction between industry and ecosystems.

[0052] Before constructing and solving the spatial simultaneous equations for the interactions between industries and ecosystems, it is necessary to test the selected influencing factors for multicollinearity to avoid overfitting caused by high correlations between variables. This example uses the Variance Inflation Factor (VIF) to test for multicollinearity between variables. If the VIF value corresponding to a variable exceeds 10, it indicates severe multicollinearity between the variables and is removed. Otherwise, it is retained.

[0053] A spatial simultaneous equation system of the interaction between industry and ecosystem is constructed to quantify the feedback loop relationship between industry and ecosystem services. The specific form is as follows: ; Among them, the water yield equation and the soil export equation represent the relationship equation between the water production ecosystem service and industry in the study area, and the relationship equation between the sediment export ecosystem service and industry, respectively. is the water-producing ecosystem service as the explained variable, is the sediment export ecosystem service as the explained variable. and They are and The spatial hysteresis factor is used to characterize the compound effects of ecosystem services caused by different spatial observation locations. is a climate driving factor, Economic drivers, is the vegetation driving factor, is the landscape driving factor. Based on the selected industry and ecosystem influencing factors, the equations can be further refined as follows: ; Where, and are the intercept terms of the water production ecosystem service equation and the sediment output ecosystem service equation, and For the corresponding and The estimated coefficients of the driving factors, and The error terms of the water production ecosystem service equation and the sediment output ecosystem service equation, respectively.

[0054] According to the collected impact factor data, the fitting results of the equation group are as follows Figure 8 As shown in Figure 2, based on the fitting results, we can identify the factors that have the greatest impact on WY and SE, as well as the interaction intensity between WY, SE and related influencing factors.

[0055] The Berndt (assessment of the joint explanatory power of multiple equations), McElroy (rationality of variable selection), and Judge (verification of the overall effectiveness of the system of equations) criteria were used to calculate the system R of the spatial simultaneous equations of the interaction between industry and ecosystem. 2 (overall goodness of fit assessment), adjusted R 2 (True goodness of fit evaluation after eliminating the influence of the number of variables), F test (overall significance of the model), and Chi-squared test (consistency between actual results and expected assumptions) are used to evaluate the model's fitting performance. Figure 9 According to the fitting equation, the feedback loop relationship between the Loess Plateau industry and ecosystem services is finally constructed, as shown in Figure 10 shown.

Claims

1. A method for quantifying the interaction between industries and ecosystems considering spatial adjacency, characterized by: The following steps are involved: S1, rasterize the study area and collect data for each grid in time series, including land data, meteorological data, vegetation data, landscape data, and industrial data; S2, unify the data to the same precision, use the InVEST model to calculate the quality of ecosystem services in the smallest research unit, and calibrate the calculation results; S3, determining the spatial adjacency relationship between the minimum research units, performing ESDA analysis on the calculation results, and determining the spatial correlation of ecosystem services between the minimum research units; In step S4, based on the spatial simultaneous equation modeling technology, the explanatory variables and explained variables are determined, and based on the data collected in step S1, a set of spatial simultaneous equations for the interaction between industry and ecosystem is constructed and solved.

2. The method for quantifying the interaction between industries and ecosystems considering spatial adjacency according to claim 1, characterized in that: In step S1, the land data include land use and land cover data, digital elevation, root restriction layer depth, and soil erosion index; the meteorological data include rainfall, evapotranspiration, and rainfall erosion index; the vegetation data include root restriction layer depth, plant available water content, and vegetation cover normalization index; the landscape data are calculated from the land use and land cover data, including the number of NP patches, PARA average perimeter-area ratio, SHDI Shannon diversity index, and AI aggregation index; the industry data include the output value of the research industry.

3. The method for quantifying the interaction between industries and ecosystems considering spatial adjacency according to claim 1, characterized in that: The data in step S2 are unified from high precision to low precision and are complete at the boundaries of each grid; at least two ecosystem services are calculated within each minimum research unit.

4. The method for quantifying the interaction between industries and ecosystems considering spatial adjacency according to claim 1 is characterized by: The spatial adjacency relationship described in step S3 includes edge adjacency, corner adjacency, distance adjacency, and inverse distance adjacency; the determined spatial adjacency relationship is determined based on the vector topological shape of the smallest research unit in the research area.

5. The method for quantifying the interaction between industries and ecosystems considering spatial adjacency according to claim 1 is characterized by: In step S3, the ESDA analysis is to calculate the Moran index of the ecosystem service quality between the minimum research units and determine whether the ecosystem services between the minimum research units are positively spatially correlated, negatively spatially correlated, or randomly distributed.

6. The method for quantifying the interaction between industries and ecosystems considering spatial adjacency according to claim 1, characterized in that: Verifying the spatial correlation determined in step S3 by Monte Carlo simulation; When the Monte Carlo simulation verification result is consistent with the spatial correlation result determined in step S3, step S4 is executed.

7. The method for quantifying the interaction between industries and ecosystems considering spatial adjacency according to claim 1, characterized in that: The explained variable in step S4 is ecosystem service; the explanatory variable is the influencing factor of the interaction between industry and ecosystem.

8. The method for quantifying the interaction between industries and ecosystems considering spatial adjacency according to claim 1 is characterized by: When solving the spatial simultaneous equations of the interactive relationship between the industry and the ecosystem, the generalized three-stage spatial least squares estimation method is used to estimate the parameters of the spatial simultaneous equations.

9. The method for quantifying the interaction between industries and ecosystems considering spatial adjacency according to claim 7, characterized in that: The influencing factors are selected and determined from the data collected in step S1, and the influencing factors are subject to a multicollinearity test.

10. The method for quantifying the interaction between industries and ecosystems considering spatial adjacency according to claim 1, characterized in that: The Berndt, McElroy and Judge criteria were used to evaluate the fitting performance of the solutions to the spatial simultaneous equations of the interaction between the industry and the ecosystem.

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