A method for measuring non-equilibrium of basic public services in rural and pastoral areas and quantitative attribution

Abstract

The application discloses a method for measuring non-equilibrium and quantitative attribution of environmental basic public services in farming and pastoral areas, which takes a town resident site as a research object, adopts three characteristic indexes of life garbage treatment, life sewage treatment and sanitary toilet, adopts an environmental basic public service allocation level index and a factor allocation rate algorithm, constructs a village environmental basic public service and social economic data set in farming and pastoral areas, and measures non-equilibrium on the basis of multi-scale description, and uses a logistic regression model to measure the causes of environmental basic public service allocation failure and the heterogeneity among factors. The application can quantitatively measure the overall and factor non-equilibrium of the village environmental basic public service on a multi-scale, and can quantitatively analyze the internal and external causes of the non-equilibrium, thereby laying a foundation for optimizing the allocation mode in farming and pastoral areas and customizing an environmental risk adaptive management scheme.

Description

Technical Field

[0001] This invention relates to the field of ecological environment governance, and in particular to a method for measuring and quantitatively attributing the imbalance of basic public environmental services in agricultural and pastoral areas. Background Technology

[0002] The environment is the foundation upon which humanity depends for survival and development, and a safe, livable, and healthy environment is one of people's basic needs. Basic environmental public services, as public service products provided to ensure the basic environmental quality for citizens' survival and development, are mainly supplied by the government through public financial investment and are an important component of the national basic public service system. Because rural public investment has long prioritized infrastructure closely related to production and daily life, such as roads, irrigation, and drinking water facilities, environmental infrastructure such as sewage treatment, garbage disposal, and sanitary toilets is weak, resulting in insufficient allocation of basic environmental public services in rural areas. This imbalance is particularly pronounced in the agricultural and pastoral areas of the Qinghai-Tibet Plateau. With the rapid growth of the resident population and the influx of tourists, and the improvement of farmers' and herders' living and consumption levels, the generation of various anthropogenic pollutants is increasing rapidly. The imbalance in the allocation of basic environmental public services leads to untimely treatment of local environmental pollution, which not only hinders the improvement of the local living environment and endangers the lives and health of residents, but also causes irreversible damage to the already fragile ecosystem of the Qinghai-Tibet Plateau.

[0003] However, the reasons for the uneven distribution of basic public services in agricultural and pastoral areas with unique natural geographical endowments and socio-economic conditions are still unclear, and there is a gap in related research in this field.

[0004] Therefore, there is an urgent need to explore the uneven distribution of basic public environmental services and the hindering factors that lead to the failure of service provision, so as to further improve the equalization of basic public environmental services for local farmers and herdsmen and improve the quality of their living environment, thereby alleviating environmental pressure and preventing ecological risks in agricultural and pastoral areas such as the Qinghai-Tibet Plateau. At present, there are no publicly available methods to measure the uneven distribution of basic public environmental services in agricultural and pastoral areas and to provide quantitative attribution methods.

[0005] Therefore, those skilled in the art are dedicated to developing a method for measuring and quantitatively attributing the imbalance of basic public services in agricultural and pastoral areas, in order to address the shortcomings of the existing technologies. Summary of the Invention

[0006] In view of the above-mentioned deficiencies of the prior art, the technical problem to be solved by the present invention is the lack of a method for measuring the unevenness of basic public services in agricultural and pastoral areas.

[0007] To achieve the above objectives, this invention provides a method for measuring and quantitatively attributing the imbalance of basic public environmental services in rural and pastoral areas. This method includes taking village and town settlements as the research object; selecting three basic public environmental service configurations—domestic waste disposal, domestic sewage treatment, and sanitary toilets—as characteristic indicators; constructing a dataset of basic public environmental services and socio-economic data in rural and pastoral villages; and, based on multi-scale characterization of imbalance, using a logistic regression model to measure the causes of failure in the configuration of basic public environmental services and the heterogeneity among its elements.

[0008] Specifically, the following steps are included:

[0009] Step 1: Using village and town settlements as the basic research unit, collect basic data on village and town settlements and construct a database;

[0010] Step 2: Determine the measurement indicators; specifically, these include three indicators from the national agricultural census: "whether domestic waste is centrally treated," "whether domestic sewage is centrally treated," and "whether the renovation of household toilets has been completed."

[0011] Step 3: Assign values ​​to the indicators in Step 2 individually. Specifically, if "Yes" is selected, the value of the indicator is "1"; if "No" is selected, the value of the indicator is "0".

[0012] Step 4: Accumulate the values ​​of individual elements to obtain the configuration level of Environmental Basic Public Service (EBPS). The value range of EBPS is [0,3].

[0013] When EBPS=3, it indicates that the village or town has well-equipped domestic waste disposal services, domestic sewage treatment services, and sanitary toilet facilities, and the level of basic public environmental services is "high".

[0014] When EBPS=2, it indicates that the configuration of two of the following basic public environmental services in the village or town is "medium": domestic waste disposal service, domestic sewage treatment service, and sanitary toilet facilities.

[0015] When EBPS=1, it indicates that the village or town has only one of the following services: domestic waste disposal service, domestic sewage treatment service, and sanitary toilet facilities: the configuration level of basic public environmental services is "low".

[0016] When EBPS = 0, it means that the three types of facilities have not yet been configured, and the configuration level of basic environmental public services is "extremely low". Step 5: Use administrative divisions at all levels to summarize and statistically analyze the configuration status of basic environmental public services in villages and towns, and calculate the EBPS (i.e., the average EBPS of all villages and towns in the jurisdiction) at various scales such as townships, counties, prefectures, and provinces, as well as the configuration rate of individual environmental infrastructure elements (such as the proportion of villages and towns in the jurisdiction that have achieved centralized treatment of domestic waste to all villages and towns); realize the expression of the uneven configuration of basic environmental public services at all levels in the agricultural and pastoral areas of the Qinghai-Tibet Plateau;

[0017] Step 6: Use an ordinal logistic regression model with EBPS as the explained variable to quantitatively attribute the imbalance, as shown in equation (1).

[0018]

[0019] In formula (1)

[0020] j = 1, 2, 3, 4, representing four levels of the configuration level of basic public services in the environment;

[0021] P(z≤j|x i ) represents the cumulative probability of categories at or below configuration level j;

[0022] β0 is the intercept term;

[0023] x i These are various explanatory variables that affect configuration levels;

[0024] β i These are the regression coefficients;

[0025] Step 7: Set up binary variables based on whether domestic waste disposal services, domestic sewage treatment services, and sanitary toilet renovations have been implemented, in order to further decompose the differences in the factors affecting the configuration of basic public environmental services in rural and town settlements. That is, "1" indicates that the configuration of the facility has been implemented, and "0" indicates that the configuration has not been implemented.

[0026] The analysis was performed using a binary logistic regression model; the model expression is shown in equation (2) below:

[0027]

[0028] In equation (2),

[0029] P(x=1|x i ) represents the probability of achieving a certain element facility configuration, with a value range of [0,1].

[0030] k represents the number of variables;

[0031] α0 is the intercept term;

[0032] x i These are various explanatory variables that affect configuration levels;

[0033] αi is the regression coefficient;

[0034] Furthermore, in step 1, the data involved mainly includes basic geographic element data and socio-economic and environmental basic public service configuration data;

[0035] Furthermore, in step 1, the basic geographic element data includes a digital elevation model (DEM) and administrative division maps at all levels;

[0036] In a specific embodiment of the present invention, in step 6, the main factors affecting the configuration of basic public services for the environment include four categories of factors, totaling nine variables: configuration cost factors, demographic and social factors, economic income factors, and grassroots management factors.

[0037] In a specific embodiment of the present invention, step 6 includes three variables: altitude classification, topographic attributes, and distance classification from the furthest settlement.

[0038] In a specific embodiment of the present invention, in step 6, the population and social factors include two variables: ethnic agglomeration attributes and resident population size classification.

[0039] In a specific embodiment of the present invention, in step 6, the economic income factors include a graded variable of the village collective income for the whole year;

[0040] In a specific embodiment of the present invention, step 6 includes two variables: the number and classification of grassroots administrative personnel and the educational attributes of grassroots leaders.

[0041] The topographic and geomorphological attributes, ethnic settlement attributes, and education attributes of village-level leaders are categorical variables, while the rest are continuous variables.

[0042] In a specific embodiment of the present invention, in step 6, the altitude classification variable of the configuration cost factor is based on the average altitude classification within the village area calculated by DEM, and the classification thresholds include <2500m, 2500~3000m, 3000~3500m, 3500~4000m, and ≥4000m.

[0043] When the altitude is less than 2500m, the frequency is 339, and the proportion is 7.86%.

[0044] At an altitude of 2500–3000m, the frequency was 1496, and the proportion was 34.67%.

[0045] At an altitude of 3000–3500 m, the frequency was 1244, and the proportion was 28.83%.

[0046] At an altitude of 3500–4000m, the frequency was 629, and the proportion was 14.58%.

[0047] When the altitude is ≥4000m, the frequency is 607, and the proportion is 14.07%;

[0048] In a specific embodiment of the present invention, in step 6, the topographic and geomorphic attribute variables of the configuration cost factors are classified according to the topographic and geomorphic characteristics of the village. If there are multiple topographic features in the village, they are identified according to the category with the largest area, which is divided into three categories: plains, hills, and mountains.

[0049] When the classification was for plains, the frequency was 389, and the proportion was 9.02%.

[0050] When the classification was hilly, the frequency was 192, and the proportion was 4.45%.

[0051] When the classification was for mountainous areas, the frequency was 3734, accounting for 86.54%;

[0052] In a specific embodiment of the present invention, in step 6, the classification variable of the farthest residential point traffic distance of the configuration cost factor is classified according to the traffic distance from the village committee to the farthest natural village or residential settlement in the village, and the classification thresholds include <1km, 1~5km, 5~10km, 10~20km, and ≥20km.

[0053] When the traffic distance is less than 1km, the frequency is 1367, accounting for 31.68%;

[0054] When the traffic distance is 1-5km, the frequency is 1979, accounting for 45.86%;

[0055] When the traffic distance is 5-10km, the frequency is 254, and the proportion is 5.89%.

[0056] When the traffic distance is 10-20km, the frequency is 250, and the proportion is 5.79%.

[0057] When the traffic distance is ≥20km, the frequency is 465, and the proportion is 10.78%.

[0058] In a specific embodiment of the present invention, in step 6, the ethnic settlement attribute variable of the population and social factors is classified by whether the number of ethnic minorities accounts for more than 30% of the total population of the village. If more than 30% is the village of ethnic minorities, it is the village of Han Chinese settlement.

[0059] The number of ethnic minority villages was 2,355, accounting for 54.58% of the total.

[0060] The number of Han Chinese-populated villages was 1960, accounting for 45.42%;

[0061] In a specific embodiment of the present invention, in step 6, the resident population size classification variable of the population and social factors is classified according to the number of resident population in the village in the census year, and the classification thresholds include <500 people, 500-1000 people, 1000-1500 people, 1500-2000 people, and ≥2000 people.

[0062] For those with a permanent resident population of less than 500, the frequency was 1273, accounting for 29.50%;

[0063] The number of permanent residents with a population of 500 to 1000 was 1488, accounting for 34.48%;

[0064] The number of permanent residents with a population of 1,000 to 1,500 was 851, accounting for 19.72% of the total.

[0065] The number of permanent residents with a population of 1,500 to 2,000 was 366, accounting for 8.48% of the total.

[0066] The number of permanent residents with a population of ≥2000 was 337, accounting for 7.81%;

[0067] In a specific embodiment of the present invention, in step 6, the annual village collective income classification variable of the economic income factor is classified according to the annual village collective income of the census year, and the classification thresholds include 0 yuan (no collective income), 10,000 to 50,000 yuan, 50,000 to 100,000 yuan, 100,000 to 200,000 yuan, and ≥200,000 yuan.

[0068] The number of villages with a total annual collective income of 0 yuan in the census year was 1139, accounting for 26.40%.

[0069] The number of villages with an annual collective income of 10,000 to 50,000 yuan in the census year was 1,813, accounting for 42.02%;

[0070] The number of villages with an annual collective income of 50,000 to 100,000 yuan in the census year was 645, accounting for 14.95%.

[0071] The number of villages with an annual collective income of 100,000 to 200,000 yuan in the census year was 370, accounting for 8.57%;

[0072] The number of villages with an annual collective income of ≥200,000 yuan in the census year was 348, accounting for 8.06%;

[0073] In a specific embodiment of the present invention, in step 6, the grassroots management factor of the grassroots administrative management personnel quantity classification variable is classified according to the number of village cadres responsible for managing village-level administrative affairs at the end of the year, and the classification thresholds include 1-2 people, 3-4 people, 5-7 people, 8-9 people, and ≥10 people;

[0074] The number of village cadres responsible for managing village-level administrative affairs at the end of the year was 1 to 2, with a frequency of 338, accounting for 7.83%.

[0075] The number of village cadres responsible for managing village-level administrative affairs at the end of the year was 3 to 4, with a frequency of 1380, accounting for 31.98%.

[0076] The number of village cadres responsible for managing village-level administrative affairs at the end of the year was 5 to 7, with a frequency of 1714, accounting for 39.72%.

[0077] The number of village cadres responsible for managing village-level administrative affairs at the end of the year was 8 to 9, with a frequency of 554, accounting for 12.84%.

[0078] The number of village cadres responsible for managing village-level administrative affairs at the end of the year was ≥10, with a frequency of 329 and a proportion of 7.62%.

[0079] In a specific embodiment of the present invention, in step 6, the grassroots leadership education attribute variable of the grassroots management factors is classified according to the highest education level of the village party branch secretary and the village committee director, including five categories: no schooling, primary school, junior high school, high school or vocational school, and junior college and above.

[0080] The highest level of education among the village Party branch secretaries and village committee directors was no schooling, with a frequency of 56 cases and a proportion of 1.30%.

[0081] The highest level of education for village Party branch secretaries and village committee directors was primary school, with a frequency of 946 cases, accounting for 21.92%.

[0082] The highest level of education for village Party branch secretaries and village committee directors was junior high school, with a frequency of 1865 cases, accounting for 43.22%.

[0083] The highest level of education for village Party branch secretaries and village committee directors was high school or vocational school, with a frequency of 1161 cases, accounting for 26.91%.

[0084] The highest level of education for village Party branch secretaries and village committee directors was college degree or above, with a frequency of 287 cases, accounting for 6.65%.

[0085] Using the above scheme, the present invention discloses a method for measuring and quantitatively attributing the imbalance of basic public services in agricultural and pastoral areas, which has the following advantages:

[0086] This invention presents a method for measuring and quantitatively attributing the imbalance of basic public environmental services in rural and pastoral areas. Addressing the insufficient analysis of the micro-level mechanisms underlying the imbalance and supply failure of basic public environmental services in rural and pastoral villages and towns, this invention quantitatively reveals the imbalance and its causes. Taking village and town settlements as the research object, it selects three basic public environmental service configurations—domestic waste treatment, domestic sewage treatment, and sanitary toilets—as characteristic indicators to construct a dataset of basic public environmental services and socio-economic data in rural and pastoral villages. Based on multi-scale characterization of imbalance, it uses a logistic regression model to measure the causes of failure in the configuration of basic public environmental services and the heterogeneity among its elements. This facilitates further discussion of configuration models for basic public environmental services suitable for the dispersed distribution of rural and pastoral settlements and the pollution characteristics of farmers and herders, providing a scientific reference for enhancing the comprehensive carrying capacity of environmental infrastructure and creating a high-quality living environment in rural and pastoral areas.

[0087] The following will further explain the concept, specific technical solution and technical effects of the present invention in conjunction with specific embodiments, so as to fully understand the purpose, features and effects of the present invention. Attached Figure Description

[0088] Figure 1 This is a map showing the elevation and distribution of various villages and towns in Qinghai Province, as described in Embodiment 1 of the present invention.

[0089] Figure 2 This is a county-level distribution map of the configuration level of basic public services for rural environment in Qinghai Province, as shown in Embodiment 1 of the present invention.

[0090] Figure 3 This is a single-element statistical chart of the configuration of basic public services in rural settlements in Embodiment 1 of the present invention;

[0091] Figure 2 In the map, a represents the county-level distribution map of the basic public environmental service configuration level index; b represents the county-level distribution map of the configuration rate of domestic waste treatment facilities; c represents the county-level distribution map of the configuration rate of domestic sewage treatment facilities; and d represents the county-level distribution map of the configuration rate of sanitary toilets.

[0092] Figure 3 In the chart, a is a statistical chart of the configuration rate (%) of single-element facilities; b is a statistical chart of the proportion of the population served by single-element facilities (%). Detailed Implementation

[0093] The following describes several preferred embodiments of the present invention to make its technical content clearer and easier to understand. The present invention can be embodied in many different forms of embodiments, which are described exemplarily, and the scope of protection of the present invention is not limited to the embodiments mentioned herein.

[0094] Example 1

[0095] like Figure 1 As shown, taking Qinghai Province as a case study, the examples illustrate that rural settlements include three categories: county towns, township seats, and village settlements.

[0096] Step 1: Using villages and towns as the basic research unit, collect basic data on 4,315 villages and towns in Qinghai Province and construct a database. The data mainly includes the following two categories: ① Basic geographic element data, including Digital Elevation Model (DEM) and administrative division maps at all levels, sourced from the Resource and Environmental Science Data Center of the Chinese Academy of Sciences. The DEM raster data has a resolution of 30m×30m. ② Data on the allocation of basic public services for socio-economic and environmental protection, mainly from the Third National Agricultural Census ledger (2016). For some missing or incomplete data, such as data on ethnic minority settlements and the permanent population of villages and towns, supplementary data were collected and verified from provincial and municipal statistical departments. Step 2: Determine the measurement indicators; specifically, these include three indicator factors from the National Agricultural Census: "whether domestic waste is centrally treated," "whether domestic sewage is centrally treated," and "whether the renovation of sanitary toilets in households has been completed."

[0097] Step 3: Assign values ​​to the indicators in Step 2 individually. Specifically, if "Yes" is selected, the value of the indicator is "1"; if "No" is selected, the value of the indicator is "0".

[0098] Step 4: Accumulate the values ​​of individual elements to obtain the configuration level of Environmental Basic Public Service (EBPS). The value range of EBPS is [0,3].

[0099] When EBPS=3, it indicates that the village or town has well-equipped domestic waste disposal services, domestic sewage treatment services, and sanitary toilet facilities, and the level of basic public environmental services is "high".

[0100] When EBPS=2, it indicates that the configuration of two of the following basic public environmental services in the village or town is "medium": domestic waste disposal service, domestic sewage treatment service, and sanitary toilet facilities.

[0101] When EBPS=1, it indicates that the village or town has only one of the following services: domestic waste disposal service, domestic sewage treatment service, and sanitary toilet facilities: the configuration level of basic public environmental services is "low".

[0102] When EBPS=0, it means that the three types of facilities have not yet been configured, and the configuration level of basic public environmental services is "extremely low".

[0103] Step 5: Summarize and statistically analyze the configuration of basic environmental public services in villages and towns at various administrative levels, and calculate the EBPS (i.e., the average EBPS of all villages and towns within the jurisdiction) at various scales such as townships, counties, prefectures, and provinces, as well as the configuration rate of individual environmental infrastructure elements (such as the proportion of villages and towns with centralized domestic waste treatment in the jurisdiction to all villages and towns); to realize the non-equilibrium of the configuration of basic environmental public services at various levels in the agricultural and pastoral areas of the Qinghai-Tibet Plateau;

[0104] Step 6: Use an ordinal logistic regression model with EBPS as the explained variable to quantitatively attribute the imbalance, as shown in equation (1).

[0105]

[0106] In formula (1)

[0107] j = 1, 2, 3, 4, representing four levels of the configuration level of basic public services in the environment;

[0108] P(z≤j|x i ) represents the cumulative probability of categories at or below configuration level j;

[0109] β0 is the intercept term;

[0110] x i These are various explanatory variables that affect configuration levels;

[0111] β i These are the regression coefficients;

[0112] As shown in Table 1, the main factors affecting the allocation of basic public environmental services include four categories of factors, totaling nine variables: allocation cost factors, demographic and social factors, economic income factors, and grassroots management factors.

[0113] The configuration cost factors include three variables: altitude classification, topographic attributes, and transportation distance classification of the farthest settlement.

[0114] The demographic and social factors mentioned include two variables: ethnicity and resident population size classification.

[0115] The economic income factors include the annual village collective income tiered variable;

[0116] The grassroots management factors include two variables: the number and level of grassroots administrative personnel and the educational background of grassroots leaders.

[0117] The topographic and geomorphological attributes, ethnic settlement attributes, and education attributes of village-level leaders are categorical variables, while the rest are continuous variables.

[0118] The altitude classification variable for the configuration cost factor is based on the average altitude within the village area calculated by DEM. The classification thresholds include <2500m, 2500~3000m, 3000~3500m, 3500~4000m, and ≥4000m.

[0119] When the altitude is less than 2500m, the frequency is 339, and the proportion is 7.86%.

[0120] At an altitude of 2500–3000m, the frequency was 1496, and the proportion was 34.67%.

[0121] At an altitude of 3000–3500 m, the frequency was 1244, and the proportion was 28.83%.

[0122] At an altitude of 3500–4000m, the frequency was 629, and the proportion was 14.58%.

[0123] When the altitude is ≥4000m, the frequency is 607, and the proportion is 14.07%;

[0124] The topographic and geomorphic attribute variables of the configuration cost factors are classified according to the topographic and geomorphic characteristics of the village. If there are multiple topographic features in the village, they are identified according to the category with the largest area, which is divided into three categories: plains, hills, and mountains.

[0125] When the classification was for plains, the frequency was 389, and the proportion was 9.02%.

[0126] When the classification was hilly, the frequency was 192, and the proportion was 4.45%.

[0127] When the classification was for mountainous areas, the frequency was 3734, accounting for 86.54%;

[0128] The configuration cost factor is classified according to the transportation distance from the village committee to the farthest natural village or residential settlement in the village. The classification thresholds include <1km, 1~5km, 5~10km, 10~20km, and ≥20km.

[0129] When the traffic distance is less than 1km, the frequency is 1367, accounting for 31.68%;

[0130] When the traffic distance is 1-5km, the frequency is 1979, accounting for 45.86%;

[0131] When the traffic distance is 5-10km, the frequency is 254, and the proportion is 5.89%.

[0132] When the traffic distance is 10-20km, the frequency is 250, and the proportion is 5.79%.

[0133] When the traffic distance is ≥20km, the frequency is 465, and the proportion is 10.78%.

[0134] The ethnic settlement attribute variable of the aforementioned population and social factors is classified by whether the number of ethnic minorities accounts for more than 30% of the total population of the village. If more than 30% is the ethnic minority settlement village, it is the Han settlement village.

[0135] The number of ethnic minority villages was 2,355, accounting for 54.58% of the total.

[0136] The number of Han Chinese-populated villages was 1960, accounting for 45.42%;

[0137] The population and social factors, specifically the resident population size classification variable, are classified according to the number of resident residents in the village during the census year. The classification thresholds include <500 people, 500-1000 people, 1000-1500 people, 1500-2000 people, and ≥2000 people.

[0138] For those with a permanent resident population of less than 500, the frequency was 1273, accounting for 29.50%;

[0139] The number of permanent residents with a population of 500 to 1000 was 1488, accounting for 34.48%;

[0140] The number of permanent residents with a population of 1,000 to 1,500 was 851, accounting for 19.72% of the total.

[0141] The number of permanent residents with a population of 1,500 to 2,000 was 366, accounting for 8.48% of the total.

[0142] The number of permanent residents with a population of ≥2000 was 337, accounting for 7.81%;

[0143] The economic income factor of the annual village collective income classification variable is classified according to the annual village collective income of the census year. The classification thresholds include 0 yuan (no collective income), 10,000 to 50,000 yuan, 50,000 to 100,000 yuan, 100,000 to 200,000 yuan, and ≥200,000 yuan.

[0144] The number of villages with a total annual collective income of 0 yuan in the census year was 1139, accounting for 26.40%.

[0145] The number of villages with an annual collective income of 10,000 to 50,000 yuan in the census year was 1,813, accounting for 42.02%;

[0146] The number of villages with an annual collective income of 50,000 to 100,000 yuan in the census year was 645, accounting for 14.95%.

[0147] The number of villages with an annual collective income of 100,000 to 200,000 yuan in the census year was 370, accounting for 8.57%;

[0148] The number of villages with an annual collective income of ≥200,000 yuan in the census year was 348, accounting for 8.06%;

[0149] The grassroots management factor, namely the number of grassroots administrative personnel, is classified according to the number of village cadres responsible for managing village-level administrative affairs at the end of the year. The classification thresholds include 1-2 people, 3-4 people, 5-7 people, 8-9 people, and ≥10 people.

[0150] The number of village cadres responsible for managing village-level administrative affairs at the end of the year was 1 to 2, with a frequency of 338, accounting for 7.83%.

[0151] The number of village cadres responsible for managing village-level administrative affairs at the end of the year was 3 to 4, with a frequency of 1380, accounting for 31.98%.

[0152] The number of village cadres responsible for managing village-level administrative affairs at the end of the year was 5 to 7, with a frequency of 1714, accounting for 39.72%.

[0153] The number of village cadres responsible for managing village-level administrative affairs at the end of the year was 8 to 9, with a frequency of 554, accounting for 12.84%.

[0154] The number of village cadres responsible for managing village-level administrative affairs at the end of the year was ≥10, with a frequency of 329 and a proportion of 7.62%.

[0155] The grassroots management factor variable of the education attribute of grassroots leaders is classified according to the highest education level of village party branch secretaries and village committee directors, including five categories: no schooling, primary school, junior high school, high school or vocational school, and junior college or above.

[0156] The highest level of education among the village Party branch secretaries and village committee directors was no schooling, with a frequency of 56 cases and a proportion of 1.30%.

[0157] The highest level of education for village Party branch secretaries and village committee directors was primary school, with a frequency of 946 cases, accounting for 21.92%.

[0158] The highest level of education for village Party branch secretaries and village committee directors was junior high school, with a frequency of 1865 cases, accounting for 43.22%.

[0159] The highest level of education for village Party branch secretaries and village committee directors was high school or vocational school, with a frequency of 1161 cases, accounting for 26.91%.

[0160] The highest level of education for village Party branch secretaries and village committee directors was college degree or above, with a frequency of 287 cases, accounting for 6.65%.

[0161] Table 1. Model Variable Names, Meanings, and Statistical Descriptions

[0162]

[0163]

[0164] Step 7: Set up binary variables based on whether domestic waste disposal services, domestic sewage treatment services, and sanitary toilet renovations have been implemented, in order to further decompose the differences in the factors affecting the configuration of basic public environmental services in rural and town settlements. That is, "1" indicates that the configuration of the facility has been implemented, and "0" indicates that the configuration has not been implemented.

[0165] The analysis was performed using a binary logistic regression model; the model expression is shown in equation (2) below:

[0166]

[0167] In equation (2),

[0168] P(y=1|x i ) represents the probability of achieving a certain element facility configuration, with a value range of [0,1].

[0169] k represents the number of variables;

[0170] α0 is the intercept term;

[0171] x i These are various explanatory variables that affect configuration levels;

[0172] α i These are the regression coefficients;

[0173] In this embodiment 1,

[0174] The county-level distribution of basic public service provision levels in rural areas of Qinghai Province is as follows: Figure 2 As shown; this indicates its overall non-equilibrium;

[0175] Single-element statistics of basic public service configuration in rural settlements, such as Figure 3 As shown, the uneven distribution of domestic sewage treatment facilities in rural and township settlements highlights the imbalance of resources.

[0176] The results of the configuration level of basic environmental public services in Example 1 are shown in Table 2. The overall and elemental imbalance of basic environmental public services in Qinghai is obvious, and there is a significant gap with the national goal of equalization and high-level configuration.

[0177] Table 2. Level of Basic Public Service Configuration in Rural and Urban Residential Areas of Qinghai Province and its Prefectures

[0178]

[0179] Quantitative attribution:

[0180] Overall estimation based on an ordered multi-class logistic regression model:

[0181] As shown in Table 3, the configuration costs, population and social factors, economic income and grassroots management factors of village and town settlements passed the significance test;

[0182] Table 3. Overall estimation results of the ordered multi-class logistic regression model. (Tab.n)

[0183]

[0184] Note: ***significance level is 0.01, **significance level is 0.05, *significance level is 0.1; the category in parentheses is the reference group for this variable.

[0185] The factor estimation results of the binary logistic regression model are shown in Table 4.

[0186] Table 4

[0187]

[0188] Note: ***significance level is 0.01, **significance level is 0.05, *significance level is 0.1; the category in parentheses is the reference group for this variable.

[0189] The ordered multi-class classification and binary logistic regression estimation models show that the imbalance of basic environmental public services in the agricultural and pastoral areas of the Qinghai-Tibet Plateau is influenced by multiple factors, including allocation costs, demographics, economic income, and grassroots management. Among these, the high altitude and remote location of the natural geography, resulting in high facility allocation costs and the failure of centralized governance, are significant causes of imbalance. For every one-level increase in local altitude and distance from settlements, the probability of EBPS (Economic Benefit System) reaching a high level decreases by 16.42% and 21.10%, respectively. The increasing returns to scale in the supply of basic environmental public services exacerbates the imbalance, while the dispersed and small-scale clustered nature of ethnic minority settlements leads to a lower priority for facility allocation. Furthermore, rising economic and income levels mean increased willingness and ability among farmers and herders to allocate basic environmental public service facilities. A larger number of grassroots administrative personnel in villages and towns, and their higher education levels, also contribute to improved allocation levels in villages and towns. This indicates that economic income and grassroots management are the internal and external driving factors for the expansion of imbalance, respectively.

[0190] In summary, this patented technical solution addresses the insufficient analysis of the micro-level causes of the imbalance and supply failure of basic public environmental services in rural and pastoral areas. This invention quantitatively reveals the imbalance and its causes in these areas. Taking rural settlements as the research object, it selects three basic public environmental service configurations—domestic waste treatment, domestic sewage treatment, and sanitary toilets—as characteristic indicators to construct a dataset of basic public environmental services and socio-economic data in rural and pastoral areas. Based on multi-scale characterization of the imbalance, it uses a logistic regression model to measure the causes of the failure of basic public environmental service configuration and the heterogeneity among its elements. This facilitates further discussion of configuration models for basic public environmental services suitable for the dispersed distribution of rural and pastoral settlements and the pollution characteristics of farmers and herders, providing a scientific reference for enhancing the comprehensive carrying capacity of environmental infrastructure and creating a high-quality living environment in rural and pastoral areas.

[0191] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A method for measuring and quantitatively attributing the imbalance of basic public environmental services in agricultural and pastoral areas, characterized in that, Includes the following steps: Step 1: Using village and town settlements as the basic research unit, collect basic data on village and town settlements and construct a database; Step 2: Determine the measurement indicators; specifically, these include three indicators from the national agricultural census: "whether domestic waste is centrally treated," "whether domestic sewage is centrally treated," and "whether the renovation of household toilets has been completed." Step 3: Assign values ​​to the indicators in Step 2 individually. Specifically, if "Yes" is selected, the value of the indicator is "1"; if "No" is selected, the value of the indicator is "0". Step 4: Accumulate the values ​​of individual elements to obtain the configuration level of Environmental Basic Public Service (EBPS), where the EBPS value range is [0,3]. When EBPS=3, it indicates that the village or town has well-equipped domestic waste disposal services, domestic sewage treatment services, and sanitary toilet facilities, and the level of basic public environmental services is "high". When EBPS=2, it indicates that the basic public environmental service configuration level of the village / town is "medium" in terms of two of the following: domestic waste disposal services, domestic sewage treatment services, and sanitary toilet facilities. When EBPS=1, it indicates that the village or town has only one of the following services: domestic waste disposal service, domestic sewage treatment service, and sanitary toilet facilities: the configuration level of basic public environmental services is "low". When EBPS=0, it means that the three types of facilities have not yet been configured, and the configuration level of basic public environmental services is "extremely low". Step 5: Summarize and statistically analyze the configuration of basic public services in villages and towns at various levels of administrative divisions, and calculate the EBPS at various scales such as townships, counties, prefectures and cities and provinces, that is, the average EBPS of all villages and towns in the jurisdiction, as well as the configuration rate of individual environmental infrastructure elements; to realize the non-equilibrium expression of the configuration of basic public services in the agricultural and pastoral areas of the Qinghai-Tibet Plateau at various levels. Step 6: Use an ordered multi-category logistic regression model, with EBPS as the explained variable, to perform quantitative attribution of the imbalance, as shown in equation (1). In formula (1) j = 1, 2, 3, 4, representing four levels of the configuration level of basic public services in the environment; P(z≤j|x i ) represents the cumulative probability of categories at or below configuration level j; β0 is the intercept term; x i These are various explanatory variables that affect configuration levels; β i These are the regression coefficients; Step 7: Set up binary variables based on whether domestic waste disposal services, domestic sewage treatment services, and sanitary toilet renovations have been implemented, in order to further decompose the differences in the factors affecting the configuration of basic public environmental services in rural and town settlements. That is, "1" indicates that the configuration of the facility has been implemented, and "0" indicates that the configuration has not been implemented. The analysis was performed using a binary logistic regression model; the model expression is as follows (2): In equation (2), P(y=1|x i ) represents the probability of achieving a certain element facility configuration, with a value range of [0,1]. k represents the number of variables; α0 is the intercept term; x i These are various explanatory variables that affect configuration levels; α i is the regression coefficient.

2. The method for measuring and quantitatively attributing the imbalance of basic public services in agricultural and pastoral areas as described in claim 1, wherein step 1 is characterized in that, The data involved mainly includes basic geographic element data and data on the allocation of basic public services in socio-economic and environmental aspects. The basic geographic element data includes digital elevation models and administrative division maps at all levels.

3. The method for measuring and quantitatively attributing the imbalance of basic public services in agricultural and pastoral areas as described in claim 1, wherein step 6 is characterized in that, The main factors influencing the allocation of basic public environmental services include four categories of factors, totaling nine variables: allocation cost factors, demographic and social factors, economic income factors, and grassroots management factors. The configuration cost factors include three variables: altitude classification, topographic attributes, and transportation distance classification of the farthest settlement. The demographic and social factors mentioned include two variables: ethnicity and resident population size classification. The economic income factors include the annual village collective income tiered variable; The grassroots management factors include two variables: the number and level of grassroots administrative personnel and the educational background of grassroots leaders. The topographic and geomorphological attributes, ethnic settlement attributes, and education attributes of village-level leaders are categorical variables, while the rest are continuous variables. The altitude classification variable for the configuration cost factor is based on the average altitude within the village area calculated by DEM. The classification thresholds include <2500m, 2500~3000m, 3000~3500m, 3500~4000m, and ≥4000m. When the altitude is less than 2500m, the frequency is 339, and the proportion is 7.86%. At an altitude of 2500–3000m, the frequency was 1496, and the proportion was 34.67%. At an altitude of 3000–3500 m, the frequency was 1244, and the proportion was 28.83%. At an altitude of 3500–4000m, the frequency was 629, and the proportion was 14.58%. When the altitude is ≥4000m, the frequency is 607, and the proportion is 14.07%; The topographic and geomorphic attribute variables of the configuration cost factors are classified according to the topographic and geomorphic characteristics of the village. If there are multiple topographic features in the village, they are identified according to the category with the largest area, which is divided into three categories: plains, hills, and mountains. When the classification was for plains, the frequency was 389, and the proportion was 9.02%. When the classification was hilly, the frequency was 192, and the proportion was 4.45%. When the classification was for mountainous areas, the frequency was 3734, accounting for 86.54%; The configuration cost factor is classified according to the transportation distance from the village committee to the farthest natural village or residential settlement in the village. The classification thresholds include <1km, 1~5km, 5~10km, 10~20km, and ≥20km. When the traffic distance is less than 1km, the frequency is 1367, accounting for 31.68%; When the traffic distance is 1-5km, the frequency is 1979, accounting for 45.86%; When the traffic distance is 5-10km, the frequency is 254, and the proportion is 5.89%. When the traffic distance is 10-20km, the frequency is 250, and the proportion is 5.79%. When the traffic distance is ≥20km, the frequency is 465, and the proportion is 10.78%. The ethnic settlement attribute variable of the aforementioned population and social factors is classified by whether the number of ethnic minorities accounts for more than 30% of the total population of the village. If more than 30% is the ethnic minority settlement village, it is the Han settlement village. The number of ethnic minority villages was 2,355, accounting for 54.58% of the total. The number of Han Chinese-populated villages was 1960, accounting for 45.42%; The population and social factors, specifically the resident population size classification variable, are classified according to the number of resident residents in the village during the census year. The classification thresholds include <500 people, 500-1000 people, 1000-1500 people, 1500-2000 people, and ≥2000 people. For those with a permanent resident population of less than 500, the frequency was 1273, accounting for 29.50%; The number of permanent residents with a population of 500 to 1000 was 1488, accounting for 34.48%; The number of permanent residents with a population of 1,000 to 1,500 was 851, accounting for 19.72% of the total. The number of permanent residents with a population of 1,500 to 2,000 was 366, accounting for 8.48% of the total. The number of permanent residents with a population of ≥2000 was 337, accounting for 7.81%; The annual village collective income grading variable of the economic income factor is graded according to the annual village collective income of the census year, and the grading thresholds include 0 yuan, 10,000 to 50,000 yuan, 50,000 to 100,000 yuan, 100,000 to 200,000 yuan, and ≥200,000 yuan. The number of villages with a total annual collective income of 0 yuan in the census year was 1139, accounting for 26.40%. The number of villages with an annual collective income of 10,000 to 50,000 yuan in the census year was 1,813, accounting for 42.02%; The number of villages with an annual collective income of 50,000 to 100,000 yuan in the census year was 645, accounting for 14.95%. The number of villages with an annual collective income of 100,000 to 200,000 yuan in the census year was 370, accounting for 8.57%; The number of villages with an annual collective income of ≥200,000 yuan in the census year was 348, accounting for 8.06%; The grassroots management factor, namely the number of grassroots administrative personnel, is classified according to the number of village cadres responsible for managing village-level administrative affairs at the end of the year. The classification thresholds include 1-2 people, 3-4 people, 5-7 people, 8-9 people, and ≥10 people. The number of village cadres responsible for managing village-level administrative affairs at the end of the year was 1 to 2, with a frequency of 338, accounting for 7.83%. The number of village cadres responsible for managing village-level administrative affairs at the end of the year was 3 to 4, with a frequency of 1380, accounting for 31.98%. The number of village cadres responsible for managing village-level administrative affairs at the end of the year was 5 to 7, with a frequency of 1714, accounting for 39.72%. The number of village cadres responsible for managing village-level administrative affairs at the end of the year was 8 to 9, with a frequency of 554, accounting for 12.84%. The number of village cadres responsible for managing village-level administrative affairs at the end of the year was ≥10, with a frequency of 329 and a proportion of 7.62%. The grassroots management factor variable of the education attribute of grassroots leaders is classified according to the highest education level of village party branch secretaries and village committee directors, including five categories: no schooling, primary school, junior high school, high school or vocational school, and junior college or above. The highest level of education among the village Party branch secretaries and village committee directors was no schooling, with a frequency of 56 cases and a proportion of 1.30%. The highest level of education for village Party branch secretaries and village committee directors was primary school, with a frequency of 946 cases, accounting for 21.92%. The highest level of education for village Party branch secretaries and village committee directors was junior high school, with a frequency of 1865 cases, accounting for 43.22%. The highest level of education for village Party branch secretaries and village committee directors was high school or vocational school, with a frequency of 1161 cases, accounting for 26.91%. The highest level of education for village Party branch secretaries and village committee directors was college degree or above, with a frequency of 287 cases, accounting for 6.65%.

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