Novel evaluation method for urbanization and rural low-carbon coupling coordination

Abstract

The invention relates to the technical field of information, and particularly discloses a novel urbanization and rural low-carbon coupling coordination evaluation method, which comprises the following steps: step S110, determining the total carbon emission amount of each rural area according to the energy carbon emission amount and the non-energy carbon emission amount of each rural area; step S120, determining a subjective weight and an objective weight according to a DEMATEL subjective weighting method and a CRI TIC objective weighting method, and determining evaluation weights of novel urbanization and rural low carbon according to the subjective weight, the objective weight, the total carbon emission amount and a game theory combination weighting method; step S130, evaluating the novel urbanization and the rural low carbon by adopting an improved matter element extension method, and determining evaluation values of the novel urbanization and the rural low carbon; the improved matter element extension method adopts the closeness degree to replace the maximum membership degree to perform grade evaluation; and step S140, establishing a coupling coordination model according to the correlation between the novel urbanization and the rural low-carbon, and measuring the coordination level.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to an evaluation method for coupling and coordinating new urbanization and rural low-carbonization. Background Art

[0002] With the in-depth development of new urbanization, its impact on the urban ecological environment has become increasingly complex. The rapid development of urbanization has led to the rapid spread and transfer of high-energy-consuming and heavily polluting enterprises to rural areas, which will hinder the development of rural low-carbonization. But at the same time, new urbanization has driven the rapid development of the economy and provided financial and technical support for the development of rural low-carbonization. On the other hand, the development of rural low-carbonization can provide cities with sustainable clean energy, improve environmental quality, and promote the urbanization process. However, under the condition of limited funds, urbanization and rural low-carbonization will produce a "seesaw effect". Investing too much money in rural low-carbonization will limit the development of urbanization. There are both positive and negative effects between new urbanization and rural low-carbonization, and the mechanism of action is complicated.

[0003] In this case, it is very important to study the coupling and coordination relationship between the two, which will help to better understand the interaction between the two complex systems, analyze the root causes of the uncoordinated development in different regions, and take appropriate measures to alleviate this phenomenon, so as to ensure the long-term coordinated development of the two systems in the future and achieve sustainable development in rural areas. In summary, it is urgent to explore the coordinated development of rural low-carbonization and new urbanization. Summary of the invention

[0004] In view of the above problems, the purpose of the present invention is to provide an evaluation method for the coupling coordination of new urbanization and rural low-carbonization, to determine the degree of interaction and synergy between new urbanization and rural low-carbonization, and to measure the coordination level between the two.

[0005] The present invention provides an evaluation method for coupling and coordinating new urbanization and rural low-carbonization, including:

[0006] Step S110, determining the total carbon emissions of rural areas in each region according to the energy carbon emissions and non-energy carbon emissions of rural areas in each region;

[0007] Step S120, determining subjective weights and objective weights according to the DEMATEL subjective weighting method and the CRI TIC objective weighting method, and determining evaluation weights of new urbanization and rural low-carbonization according to the subjective weights, objective weights, total carbon emissions, and a game theory combined weighting method;

[0008] Step S130, using an improved matter-element extension method to evaluate new urbanization and rural low-carbonization, and determine the evaluation values ​​of new urbanization and rural low-carbonization; the improved matter-element extension method uses closeness instead of maximum membership to perform grade evaluation;

[0009] Step S140: establishing a coupling coordination model based on the relationship between new urbanization and rural low-carbonization, and measuring the coordination level.

[0010] In a possible implementation, the energy carbon emissions in the rural area include the carbon emissions from energy consumption in the agricultural production process and the carbon emissions from energy consumption in rural residents' lives; the step S110 includes:

[0011] The energy carbon emissions in rural areas are determined by the following formula: i :

[0012]

[0013] In the formula, E i is the energy carbon emission of rural areas in the ith region, AE ij NCV represents the final consumption of the jth energy in the ith region. j Indicates the lower calorific value of the jth energy source, EF j represents the carbon content of the jth energy unit lower calorific value, O j represents the carbon oxidation rate of the jth energy source, and η represents the coefficient of carbon conversion into carbon dioxide;

[0014] The non-energy carbon emissions N in rural areas are determined according to the following formula i :

[0015]

[0016] Where N i is the non-energy carbon emissions of rural areas in the ith region, g ij is the amount of non-energy carbon emission source j in region i, e jk is the emission coefficient of the kth type of greenhouse gas from the jth non-energy carbon emission source, GWP k is the multiple of the CO2 warming potential on a century-scale corresponding to the kth type of greenhouse gas;

[0017] The total carbon emissions C of rural areas in the ith region are determined according to the following formula: i :

[0018] C i =E i +N i

[0019] In the formula, C i is the total carbon emissions of rural areas in the ith region, N i is the non-energy carbon emissions of rural areas in the ith region, E i is the energy carbon emission of rural areas in the ith region.

[0020] In a possible implementation, step S110 further includes: determining the carbon emission intensity CI according to the following formula: i :

[0021] CI i =C i / GDP i

[0022] In the formula, CI i is the carbon emission intensity of rural areas in the ith region, C i is the total carbon emissions of rural areas in the ith region, GDP i is the gross rural output value of the ith region.

[0023] In a possible implementation, step S120 includes:

[0024] According to the strength relationship between the influencing factors, the following direct impact matrix A is established:

[0025]

[0026] In the formula, x ij is the original element, indicating the influence of index i on index j, i,j∈{1,2,3,…,n};

[0027] The direct impact matrix A is normalized to obtain the following standard impact matrix B:

[0028]

[0029]

[0030] Where B is the normative influence matrix, b ij is the original element, i,j∈{1,2,3,…,n};

[0031] The subjective weight is calculated according to the normative impact matrix, and the formula is as follows:

[0032]

[0033]

[0034]

[0035]

[0036]

[0037] In the formula, T is the comprehensive effect, k is a constant, and D i is the influence degree, t ijis the element in row i and column j of T, F i is the degree of influence, M i is the centrality, R i is the cause degree, d i is the subjective weight of indicator i.

[0038] In a possible implementation, step S120 further includes:

[0039] The objective weight of indicator i is calculated according to the following formula:

[0040]

[0041]

[0042]

[0043]

[0044] c i =σ i R i

[0045]

[0046] In the formula, σ j is the standard deviation, r ij is the correlation coefficient, y hi represents the i-th evaluation index in the h-th evaluation object, y hj represents the jth evaluation index in the hth evaluation object, is the mean value of the i-th evaluation index corresponding to the m evaluation objects, is the mean value of the jth evaluation index corresponding to the m evaluation objects, R ij Indicates conflict, R i is the cause degree, c i In order to calculate the amount of information contained in the indicators based on their differences and conflicts, w i2 is the objective weight of indicator i.

[0047] In a possible implementation, step S120 further includes:

[0048] The best evaluation weight w is calculated according to the following formula: i :

[0049] w i =(w i1 ,w i2 ,...,w in ),i=1,2,...,p

[0050] In the formula, win is the nth weight of index i, i=1,2,...,p

[0051] The optimal comprehensive weight is calculated according to the following formula:

[0052]

[0053]

[0054] In the formula, α i is the optimization coefficient, is the coefficient, w * is the best evaluation weight, w i The transpose of .

[0055] In a possible implementation, step S130 includes:

[0056] Establish a matter-element matrix of n features, the expression is as follows:

[0057]

[0058] In the formula, R = (N, c, v) is an ordered triple, N represents the evaluation object, c represents the feature of the object, and v represents the feature value;

[0059] The evaluation index system is divided into J levels, and the corresponding classical domain and section domain matrices are obtained according to the J levels;

[0060] The expression of the classical domain matter-element is as follows:

[0061]

[0062] Where N j represents the jth level of matter-element, v ji It represents the value range of the i-th evaluation index of the matter-element at this feature level;

[0063] Section matter element R p The expression is as follows:

[0064]

[0065] Where N p represents the overall evaluation system composed of evaluation indicators, v pi Represents the domain matter-element with respect to the feature c i The value range of a pi represents the lower limit of the value interval of the ith indicator, b pi Indicates the upper limit of the value interval of the i-th indicator;

[0066] The improvement is made by using closeness instead of maximum membership. The calculation formula is as follows:

[0067]

[0068]

[0069]

[0070]

[0071]

[0072] In the formula, K j (N) Standardized form, D j (y i ) is the distance, K j (N) is the closeness, and U is the characteristic value of the grade variable of the entity to be evaluated, that is, the evaluation value of new urbanization and rural low-carbonization.

[0073] In a possible implementation, step S140 includes:

[0074] The coupling coordination model between the new urbanization and rural low-carbonization is established according to the following formula:

[0075]

[0076] T=aU1+bU2

[0077]

[0078]

[0079] Among them, C is the coupling degree of new urbanization and rural low-carbonization, T is the comprehensive evaluation index of new urbanization and rural low-carbonization, D is the coupling coordination degree of new urbanization and rural low-carbonization, β is the relative development index of new urbanization and rural low-carbonization, U1 is the comprehensive evaluation value of new urbanization, U2 is the comprehensive evaluation value of rural low-carbonization, and a and b are weight values.

[0080] In one possible implementation, the GTWR model is used to calculate the rural low-carbonization level, and the formula is as follows:

[0081]

[0082] In the formula, i and k are independent variables, i is the region, k is the new urbanization subsystem, and y i is the explanatory variable, indicating the level of rural low carbonization, x ikis the explanatory variable, indicating the development level of the new urbanization subsystem, (u i ,v i ,t i ) is the space-time coordinate, reflecting longitude, latitude and time, β0(u i ,v i ,t i ) represents a constant, β k (u i ,v i ,t i ) represents the regression coefficient, ψ i is the error term.

[0083] In one possible implementation, a random effect panel Tobit model is used for quantitative estimation. The random effect panel Tobit model has the following formula:

[0084] D it =η it +β1indu it +β2engel it +β3fina it +β4eco it +β5gov it +ε iitt

[0085] Where D it is CCD, i is region, t is time, η it is a constant term, β j is the regression coefficient, indu it For the degree of industrialization, engel it is the Engel coefficient of rural residents, it For the level of rural economic development, eco it is the level of development of rural ecosystems, gov it is the supporter capacity, ε it is a random interference term.

[0086] The evaluation method for the coupling coordination of new urbanization and rural low-carbonization provided by the present invention takes rural areas as the research subject, establishes evaluation systems for new urbanization and rural low-carbonization respectively, determines the degree of interaction and synergy between new urbanization and rural low-carbonization, and measures the coordination level between the two. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 A schematic diagram of a flow chart of an evaluation method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0088] The following detailed description of the embodiments of the present invention is further described in detail in conjunction with the accompanying drawings and examples. The detailed description of the following embodiments and the accompanying drawings are used to exemplarily illustrate the principles of the present invention, but cannot be used to limit the scope of the present invention, that is, the present invention is not limited to the preferred embodiments described, and the scope of the present invention is defined by the claims.

[0089] In the description of the present invention, it should be noted that, unless otherwise specified, “plurality” means two or more than two; the terms “first”, “second”, etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance; for ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances.

[0090] Since the development of new urbanization is concentrated in cities, the interaction with rural low-carbonization is easily overlooked. By analyzing the coupling mechanism of the two, it can be seen that the uncoordinated development of the two will pose a threat to the economic development and ecological environment of urban and rural areas, and widen the urban-rural gap. In the context of the era of carbon neutrality and revitalization, it is very necessary to study the coupling and coordination relationship between the two. The present invention incorporates new urbanization and rural low-carbonization into a comprehensive analysis framework, and analyzes the coupling relationship between the two, which not only helps to balance economic growth and the ecological environment, but also enriches the research on rural sustainable development evaluation.

[0091] Figure 1 A flow chart of the evaluation method provided by the embodiment of the present invention is shown in FIG. Figure 1 As shown in the figure, a new evaluation method for the coupling and coordination of urbanization and rural low-carbonization is provided, including:

[0092] Step S110, determining the total carbon emissions of rural areas in each region according to the energy carbon emissions and non-energy carbon emissions of rural areas in each region;

[0093] At present, the new urbanization consists of four primary indicators: population urbanization, economic urbanization, social urbanization and spatial urbanization. Among them, the population urbanization level is measured by the urbanization rate, natural population growth rate and urban population density. The changes in the urban population in the region reflect the development level of the new urbanization. The economic urbanization level is measured by per capita GDP, the proportion of the secondary industry in the regional GDP and the proportion of the tertiary industry in the regional GDP. The above indicators represent the economic development level and industrial structure of the region and are economic factors reflecting the urbanization level. The social urbanization level is measured by the registered unemployment rate of the urban population, the number of buses per 10,000 people and the number of health technicians, which reflect the employment opportunities and social security services of residents in the process of urbanization. Spatial urbanization is measured by the built-up area, per capita road area and per capita green area, which represents the level of urban construction and ecological environment in the process of urbanization.

[0094] In one possible implementation, rural carbon emissions are mainly divided into energy carbon emissions and non-energy carbon emissions. The present invention intends to evaluate rural low-carbonization through energy carbon emissions and non-energy carbon emissions.

[0095] Energy carbon emissions in rural areas include carbon emissions from energy consumption in the agricultural production process and carbon emissions from energy consumption in rural residents’ lives. Energy carbon emissions in rural areas mainly include carbon emissions from energy consumption in the agricultural production process and carbon emissions from energy consumption in rural residents’ lives. The formula is as follows:

[0096] E i =E ri +E pi

[0097] In the formula, E i represents the carbon emissions from rural energy consumption in the i-th province; E ri Represents carbon emissions related to energy consumption in agricultural production processes such as planting, forestry, animal husbandry and fishery; E pi Represents the carbon emissions from rural residents’ life energy consumption.

[0098] The calculation methods for each part of carbon emissions mainly include actual measurement, material balance and emission coefficient.

[0099] Combined with the IPCC greenhouse gas inventory method, the energy carbon emissions in rural areas are determined according to the following formula: i :

[0100]

[0101] In the formula, E i is the energy carbon emission of rural areas in the ith region, AE ij NCV represents the final consumption of the jth energy in the ith region. j Indicates the lower calorific value of the jth energy source, EF j represents the carbon content of the jth energy unit lower calorific value, O j represents the carbon oxidation rate of the jth energy source, η represents the coefficient of carbon conversion into carbon dioxide, which is 44 / 12;

[0102] Non-energy carbon emissions in rural areas mainly include agricultural material inputs, crop planting and livestock breeding, so as to establish a framework for calculating rural non-energy carbon emissions. Agricultural production is an indispensable main activity in rural areas. Agricultural production mainly uses production materials such as pesticides, fertilizers, and agricultural films. In this process, greenhouse gases such as methane and carbon dioxide are inevitably produced, which affects the low-carbonization process in rural areas. Animal breeding in rural areas is mostly based on pigs, cattle and sheep. The present invention selects pigs, cattle and sheep as carbon emission sources.

[0103] The non-energy carbon emissions N in rural areas are determined according to the following formulai :

[0104]

[0105] Where N i is the non-energy carbon emissions of rural areas in the ith region, g ij is the amount of non-energy carbon emission source j in region i, e jk is the emission coefficient of the kth type of greenhouse gas from the jth non-energy carbon emission source, GWP k is the multiple of the CO2 warming potential on a century-scale corresponding to the kth type of greenhouse gas;

[0106] According to the IPCC's 5th Assessment Report, the 100-year scale warming potentials of greenhouse gases CO2, CH4, and N2O are 1, 28, and 265 times, respectively. i The total carbon emissions C of rural areas in the ith region are determined according to the following formula: i :

[0107] C i =E i +N i

[0108] In the formula, C i is the total carbon emissions of rural areas in the ith region, N i is the non-energy carbon emissions of rural areas in the ith region, E i is the energy carbon emission of rural areas in the ith region.

[0109] In a possible implementation, in order to make different regions comparable, the present invention selects carbon emission intensity as an indicator to measure the level of rural decarbonization in each region. This value is the ratio of rural carbon emissions to the gross domestic product (GDP) of agriculture and animal husbandry.

[0110] The carbon emission intensity CI is determined according to the following formula i :

[0111] CI i =C i / GDP i

[0112] In the formula, CI i is the carbon emission intensity of rural areas in the ith region, C i is the total carbon emissions of rural areas in the ith region, GDP i is the gross rural output value of the ith region.

[0113] Step S120, determining subjective weights and objective weights according to the DEMATEL subjective weighting method and the CRITIC objective weighting method, and determining evaluation weights of new urbanization and rural low-carbonization according to the subjective weights, objective weights, total carbon emissions, and a game theory combined weighting method;

[0114] In one possible implementation, the subjective and objective weights are determined according to the DEMATEL subjective weighting method and the CRITIC objective weighting method, and then the final evaluation weight is determined through game theory combined weighting, which minimizes the deviation between the evaluation weight and the subjective and objective weights and improves the reliability of the evaluation results.

[0115] (1) Subjective weighting method - DEMATEL method

[0116] The DEMATEL method is a method that uses graph theory and matrix tools to analyze the location and mutual influence of system elements. This method evaluates the importance of each influencing factor and the relationship between the influencing factors based on the centrality and causality of each influencing factor. ij (The degree of influence of indicator i on indicator j) is scored by experts, and the quantitative degree of each factor is divided into 5 levels: no influence "0", very weak influence "1", weak influence "2", strong influence "3" and "extremely strong influence" 4". Then, combining the opinions of multiple experts and based on the strong and weak relationships between various influencing factors, the average direct influence matrix A is formed.

[0117] According to the strength relationship between the influencing factors, the following direct impact matrix A is established:

[0118]

[0119] In the formula, x ij is the original element, indicating the influence of index i on index j, i,j∈{1,2,3,…,n};

[0120] The direct impact matrix A is normalized to obtain the following standard impact matrix B:

[0121]

[0122]

[0123] Where B is the normative influence matrix, b ij is the original element, i,j∈{1,2,3,…,n};

[0124] The subjective weight is calculated according to the normative impact matrix, and the formula is as follows:

[0125]

[0126]

[0127]

[0128] M i =D i +F i

[0129] R i =D i -F i

[0130]

[0131] In the formula, T is the comprehensive effect, k is a constant, and D i is the influence degree, t ij is the element in row i and column j of T, F i is the degree of influence, M i is the centrality, R i is the cause degree, d i is the subjective weight of indicator i.

[0132] 2) Objective empowerment method - CRITIC method

[0133] The CRITIC method uses standard deviation to measure the difference of each indicator. The larger the standard deviation of the indicator, the more information it provides, and the larger the weight should be assigned. The Pearson correlation coefficient is used to describe the conflict between indicators. The larger the correlation coefficient, the smaller the conflict between indicators, which means that the two indicators share too much redundant information and do not provide additional information. Their weights should be reduced. m×n In the equation, m is the number of evaluation schemes, n is the number of evaluation indicators, and x is the uniform dimension matrix element. ij Normalize it to get y ij .

[0134] Effectiveness Index

[0135] Cost indicators

[0136] The objective weight of indicator i is calculated according to the following formula:

[0137]

[0138]

[0139]

[0140]

[0141] ci =σ i R i

[0142]

[0143] In the formula, σ j is the standard deviation, r ij is the correlation coefficient, y hi represents the i-th evaluation index in the h-th evaluation object, y hj represents the jth evaluation index in the hth evaluation object, is the mean value of the i-th evaluation index corresponding to the m evaluation objects, is the mean value of the jth evaluation index corresponding to the m evaluation objects, R ij Indicates conflict, R i is the cause degree, c i In order to calculate the amount of information contained in the indicators based on their differences and conflicts, w i2 is the objective weight of indicator i.

[0144] (3) Combination weighting based on game theory

[0145] Assuming that the weights of n indicators are determined by p methods, the indicator weight vector is: w i =(w i1 ,w i2 ,...,w in ),i=1,2,...,p; where w in For i = 1, 2, ..., p. Any linear combination of weight vectors is expressed as By optimizing the coefficient α i , so that W and w i The deviation of is minimized, that is The optimal first-order derivative derived from the differential properties of the matrix is:

[0146]

[0147]

[0148] The coefficient α i Normalize and get the optimal comprehensive weight

[0149] The optimal comprehensive weight is calculated according to the following formula:

[0150]

[0151]

[0152] In the formula, α iis the optimization coefficient, is the coefficient, w * is the best evaluation weight, w i The transpose of .

[0153] Step S130, using an improved matter-element extension method to evaluate new urbanization and rural low-carbonization, and determine the evaluation values ​​of new urbanization and rural low-carbonization;

[0154] In a possible implementation, the traditional matter-element extension model usually adopts the maximum membership principle to assess the evaluation level, but this method cannot objectively reflect the fuzziness of the critical interval of the evaluation object, and in some cases it is easy to cause the evaluation information to be missing, resulting in invalid evaluation results. Therefore, in order to obtain more accurate evaluation results, it is necessary to improve the maximum membership criterion of the model. The improved matter-element extension method uses closeness instead of maximum membership for grade evaluation. Through the evaluation level and measurement data, the method generates the classic domain, section domain and closeness. Finally, the constructed model is used to obtain the characteristic value for a comprehensive quantitative evaluation. The present invention adopts an improved matter-element extension method to evaluate the development level of new urbanization and rural low-carbonization in different time and space. The improved matter-element extension method uses closeness instead of maximum membership for grade evaluation.

[0155] (1) Constructing matter-element matrix

[0156] Matter-element is the abbreviation of the three basic elements that describe the name, characteristics and magnitude of things.

[0157] Establish a matter-element matrix of n features, the expression is as follows:

[0158]

[0159] In the formula, R = (N, c, v) is an ordered triple, N represents the evaluation object, c represents the feature of the object, and v represents the feature value;

[0160] (2) Determine the classical domain and the nodal domain

[0161] The evaluation index system is divided into J levels, and the corresponding classical domain and section domain matrices are obtained according to the established J levels. The classical domain represents the corresponding value range of the characteristics of the object to be evaluated at each level, and the section domain represents the data range of the corresponding characteristics of the object to be evaluated. The corresponding classical domain and section domain matrices can be obtained according to the established evaluation criteria.

[0162] The expression of the classical domain matter-element is as follows:

[0163]

[0164] Where N j represents the jth level of matter-element, vji It represents the value range of the i-th evaluation index of the matter-element at this feature level;

[0165] Section matter element R p The expression is as follows:

[0166]

[0167] Where N p represents the overall evaluation system composed of evaluation indicators, v pi Represents the domain matter-element with respect to the feature c i The value range of a pi represents the lower limit of the value interval of the ith indicator, b pi Indicates the upper limit of the value range of the i-th indicator.

[0168] (3) Calculate the level of closeness

[0169] The improvement is made by using closeness instead of maximum membership. The calculation formula is as follows:

[0170]

[0171]

[0172] (4) Calculate eigenvalues

[0173] The eigenvalue of the grade variable represents the degree to which the object-element to be evaluated is biased towards the adjacent grade, and at the same time reflects the quality of the evaluation system, that is, the evaluation value. First, the degree of closeness of the object-element to be evaluated to each evaluation grade is standardized to obtain Then, the hierarchical variable characteristic value U of the entity to be evaluated is calculated, which represents the evaluation value of new urbanization and rural low-carbonization in this article.

[0174]

[0175]

[0176]

[0177] In the formula, K j (N) Standardized form, D j (y i ) is the distance, K j (N) is the closeness, and U is the characteristic value of the grade variable of the entity to be evaluated, that is, the evaluation value of new urbanization and rural low-carbonization.

[0178] Step S140: establishing a coupling coordination model based on the relationship between new urbanization and rural low-carbonization, and measuring the coordination level.

[0179] In one possible implementation, the coupling coordination model is used to measure the degree of interaction and synergy between two or more systems, which determines the development trend of the integrated system from disorder to order, including coupling degree and coupling coordination degree. The coupling degree is used to reflect the strength of the mutual influence and interaction between two systems or internal elements, but it cannot explain the development level between systems. For example, the coupling degree between the new urbanization and rural low-carbonization subsystems may be high, while the overall development level may be low, which is not the optimal state for high-quality urbanization and low-carbon development. The coordination degree mainly measures the degree of harmony between systems or internal elements in the development process. The coupling coordination model is used to study the relationship between the new urbanization and rural low-carbonization, and measure the coordination level between the two.

[0180] The coupling coordination model between the new urbanization and rural low-carbonization is established according to the following formula:

[0181]

[0182] T=aU1+bU2

[0183]

[0184]

[0185] Among them, C is the coupling degree of new urbanization and rural low-carbonization, T is the comprehensive evaluation index of new urbanization and rural low-carbonization, D is the coupling coordination degree of new urbanization and rural low-carbonization, β is the relative development index of new urbanization and rural low-carbonization, U1 is the comprehensive evaluation value of new urbanization, U2 is the comprehensive evaluation value of rural low-carbonization, and a and b are weight values. Assuming that the subsystems have equal status, the same weight is given to the two, that is, a = b = 0.5.

[0186] In one possible implementation, GTWR has been widely used to measure the degree of influence on explanatory variables. It has obvious advantages over traditional statistical models (such as spatial econometric models) in reflecting the spatiotemporal heterogeneity of different regions. Among them, GTWR can more intuitively display the statistical relationship between variables at any time in each sample area, thereby effectively reflecting the evolutionary relationship between variables in the spatiotemporal scene. Therefore, in order to better analyze the spatiotemporal heterogeneity of the study area, this study uses the GTWR model to explore the internal mechanism of the four subsystems of new urbanization and rural decarbonization under different spatiotemporal scenarios, and on this basis, compare the differences in the effects of new urbanization and traditional urbanization on rural decarbonization.

[0187] The GTWR model is used to calculate the rural low-carbonization level. The formula is as follows:

[0188]

[0189] In the formula, i and k are independent variables, i is the region, k is the new urbanization subsystem, and y i is the explanatory variable, indicating the level of rural low carbonization, x ik is the explanatory variable, indicating the development level of the new urbanization subsystem, (u i ,v i ,t i ) is the space-time coordinate, reflecting longitude, latitude and time, β0(u i ,v i ,t i ) represents a constant, β k (u i ,v i ,t i ) represents the regression coefficient, ψ i is the error term.

[0190] In a possible implementation method, the coupling relationship between new urbanization and rural low-carbonization depends not only on the level of the two systems, but also on external conditions. Since the value of the coupling coordination degree between new urbanization and rural low-carbonization varies between 0 and 1, the explanatory variable has the characteristics of being cut, which meets the conditions set by the constraint dependent variable in the Tobit regression model. The study uses the random effect panel Tobit model for quantitative estimation, which can effectively avoid the result deviation caused by the least squares regression compared with the fixed effect panel Tobit model. Referring to existing studies and combining with actual conditions, representative indicators are selected from the factors affecting the coupling coordination degree, such as industrialization level, farmers' living standards, rural economic development, rural ecological environment, and financial support from supporters. The number of industrial enterprises above designated size is selected to measure the level of industrialization development; the Engel coefficient of rural residents is selected to represent the living standards of farmers; the total output value of forestry, agriculture, fishery, and animal husbandry is selected as an indicator to measure the level of rural economic development; the afforestation area is selected to characterize the development of the rural ecological environment; and the fiscal expenditure on agriculture, forestry and water affairs is selected as the fiscal support for rural development.

[0191] The random effect panel Tobit model is used for quantitative estimation. The random effect panel Tobit model is as follows:

[0192] D it =η it +β1indu it +β2engel it +β3fina it +β4eco it +β5gov it +ε iitt

[0193] Where Dit is CCD, i is region, t is time, η it is a constant term, β j is the regression coefficient, indu it For the degree of industrialization, engel it is the Engel coefficient of rural residents, it For the level of rural economic development, eco it is the level of development of rural ecosystems, gov it is the supporter capacity, ε it is a random interference term.

[0194] The evaluation method for the coupling coordination of new urbanization and rural low-carbonization provided by the present invention takes rural areas as the research subject, establishes evaluation systems for new urbanization and rural low-carbonization respectively, determines the degree of interaction and synergy between new urbanization and rural low-carbonization, and measures the coordination level between the two.

[0195] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A new type of urbanization and rural low-carbonization coupling coordination evaluation method, characterized by: include: Step S110, determining the total carbon emissions of rural areas in each region according to the energy carbon emissions and non-energy carbon emissions of rural areas in each region; Step S120, determining subjective weights and objective weights according to the DEMATEL subjective weighting method and the CRITIC objective weighting method, and determining evaluation weights of new urbanization and rural low-carbonization according to the subjective weights, objective weights, total carbon emissions, and a game theory combined weighting method; Step S130, using an improved matter-element extension method to evaluate new urbanization and rural low-carbonization, and determine the evaluation values ​​of new urbanization and rural low-carbonization; the improved matter-element extension method uses closeness instead of maximum membership to perform grade evaluation; Step S140: establishing a coupling coordination model based on the relationship between new urbanization and rural low-carbonization, and measuring the coordination level.

2. The evaluation method according to claim 1, characterized in that: The energy carbon emissions in rural areas include the carbon emissions from energy consumption in agricultural production and the carbon emissions from energy consumption in rural residents' lives; step S110 includes: The energy carbon emissions in rural areas are determined by the following formula: i : In the formula, E i is the energy carbon emission of rural areas in the ith region, AE ij NCV represents the final consumption of the jth energy in the ith region. j Indicates the lower calorific value of the jth energy source, EF j represents the carbon content of the jth energy unit lower calorific value, O j represents the carbon oxidation rate of the jth energy source, and η represents the coefficient of carbon conversion into carbon dioxide; The non-energy carbon emissions N in rural areas are determined according to the following formula i : Where N i is the non-energy carbon emissions of rural areas in the ith region, g ij is the amount of non-energy carbon emission source j in region i, e jk is the emission coefficient of the kth type of greenhouse gas from the jth non-energy carbon emission source, GWP k is the multiple of the CO2 warming potential on a century-scale corresponding to the kth type of greenhouse gas; The total carbon emissions C of rural areas in the ith region are determined according to the following formula: i : C i =E i +N i In the formula, C i is the total carbon emissions of rural areas in the ith region, N i is the non-energy carbon emissions of rural areas in the ith region, E i is the energy carbon emission of rural areas in the ith region.

3. The evaluation method according to claim 2, characterized in that: The step S110 further includes: The carbon emission intensity CI is determined according to the following formula i : CI i =C i / GDP i In the formula, CI i is the carbon emission intensity of rural areas in the ith region, C i is the total carbon emissions of rural areas in the ith region, GDP i is the gross rural output value of the ith region.

4. The evaluation method according to claim 1, characterized in that: The step S120 includes: According to the strength relationship between the influencing factors, the following direct impact matrix A is established: In the formula, x ij is the original element, indicating the influence of index i on index j, i,j∈{1,2,3,…,n}; The direct impact matrix A is normalized to obtain the following standard impact matrix B: Where B is the normative influence matrix, b ij is the original element, i,j∈{1,2,3,…,n}; The subjective weight is calculated according to the normative impact matrix, and the formula is as follows: M i =D i +F i R i =D i -F i In the formula, T is the comprehensive effect, k is a constant, and D ii is the influence degree, t ij is the element in row i and column j of T, F i is the degree of influence, M i is the centrality, R i is the cause degree, d i is the subjective weight of indicator i.

5. The evaluation method according to claim 4, characterized in that: The step S120 further includes: The objective weight of indicator i is calculated according to the following formula: c i =s i R i In the formula, σ j is the standard deviation, r ij is the correlation coefficient, y hi represents the i-th evaluation index in the h-th evaluation object, y hj represents the jth evaluation index in the hth evaluation object, is the mean value of the i-th evaluation index corresponding to the m evaluation objects, is the mean value of the jth evaluation index corresponding to the m evaluation objects, R ij Indicates conflict, R i is the cause degree, c i In order to calculate the amount of information contained in the indicators based on their differences and conflicts, w i2 is the objective weight of indicator i.

6. The evaluation method according to claim 5, characterized in that: The step S120 further includes: The best evaluation weight w is calculated according to the following formula: i : w i =(w i1 ,w i2 ,...,w in ),i=1,2,...,p In the formula, w in is the nth weight of index i, i=1,2,...,p The optimal comprehensive weight is calculated according to the following formula: In the formula, α i is the optimization coefficient, is the coefficient, w * is the best evaluation weight, w i The transpose of .

7. The evaluation method according to claim 1, characterized in that: The step S130 includes: Establish a matter-element matrix of n features, the expression is as follows: In the formula, R = (N, c, v) is an ordered triple, N represents the evaluation object, c represents the feature of the object, and v represents the feature value; The evaluation index system is divided into J levels, and the corresponding classical domain and section domain matrices are obtained according to the J levels; The expression of the classical domain matter-element is as follows: Where N j represents the jth level of matter-element, v ji It represents the value range of the ii-th evaluation index of the matter-element at this feature level; Section matter element R p The expression is as follows: Where N p represents the overall evaluation system composed of evaluation indicators, v pi Represents the domain matter-element with respect to the feature c i The value range of a pi represents the lower limit of the value interval of the ith indicator, b pi Indicates the upper limit of the value interval of the i-th indicator; The improvement is made by using closeness instead of maximum membership. The calculation formula is as follows: In the formula, K j (N) Standardized form, D j (y i ) is the distance, K j (N) is the closeness, and U is the characteristic value of the hierarchical variable of the entity to be evaluated, that is, the evaluation value of new urbanization and rural low-carbonization.

8. The evaluation method according to claim 1, characterized in that: The step S140 includes: The coupling coordination model between the new urbanization and rural low-carbonization is established according to the following formula: T=aU1+bU2 Among them, C is the coupling degree of new urbanization and rural low-carbonization, T is the comprehensive evaluation index of new urbanization and rural low-carbonization, D is the coupling coordination degree of new urbanization and rural low-carbonization, β is the relative development index of new urbanization and rural low-carbonization, U1 is the comprehensive evaluation value of new urbanization, U2 is the comprehensive evaluation value of rural low-carbonization, and a and b are weight values.

9. The evaluation method according to claim 1, characterized in that: Also includes: The GTWR model is used to calculate the rural low-carbonization level. The formula is as follows: In the formula, i and k are independent variables, i is the region, k is the new urbanization subsystem, and y i is the explanatory variable, indicating the level of rural low carbonization, x ik is the explanatory variable, indicating the development level of the new urbanization subsystem, (u i ,v i ,t ii ) is the space-time coordinate, reflecting longitude, latitude and time, β0(u i ,v i ,t i ) represents a constant, β k (u i ,v i ,t i ) represents the regression coefficient, ψ i is the error term.

10. The evaluation method according to claim 1, characterized in that: Also includes: The random effect panel Tobit model is used for quantitative estimation. The random effect panel Tobit model is as follows: D it =the it +β1indu it +β2engel it +β3fina it +β4eco it +β5gov it +e it Where D it is CCD, i is region, t is time, η it is a constant term, β j is the regression coefficient, indu it For the degree of industrialization, engel it is the Engel coefficient of rural residents, it For the level of rural economic development, eco it is the level of development of rural ecosystems, gov it is the supporter capacity, ε it is a random interference term.

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