A method, storage medium and device for evaluating total factor energy utilization efficiency

Abstract

The present invention discloses a total factor energy efficiency evaluation method, storage medium, and device, belonging to the field of energy efficiency. The method comprises: selecting appropriate input variables, output variables, and environmental variables to determine evaluation indicators; using the SBM model and the SFA model to construct a total factor energy efficiency calculation model that eliminates the influence of environmental factors; obtaining corresponding data for each area to be measured and inserting it into the model for empirical verification. The total factor energy efficiency evaluation method of the present invention fully considers the characteristics that single-factor energy efficiency cannot reflect the role of other input factors in the production and consumption process, nor can it calculate environmental pollution and other problems caused by energy consumption. It adopts more comprehensive evaluation indicators, considers various factors such as human resources, resources, environment, and economy, and constructs a multi-input and multi-output total factor energy efficiency calculation model to better reflect the energy utilization situation in various provinces and cities.

Description

Technical Field

[0001] The present invention relates to the field of energy utilization efficiency, and in particular to a method, storage medium and device for evaluating total factor energy utilization efficiency. Background Art

[0002] Currently, the primary metric for measuring energy efficiency is energy intensity. The recently proposed dual energy consumption control, which aims to reduce both total energy consumption and energy intensity, is a crucial measure for advancing ecological civilization and addressing tightening resource constraints and severe environmental pollution. Energy efficiency refers to the ratio of the useful output of a production process to the energy input involved. Energy intensity, on the other hand, is a single-factor energy efficiency measure, considering only total energy consumption and only regional GDP. It fails to reflect the role of other inputs in the production and consumption processes, nor does it consider environmental pollution caused by energy consumption. For example, if a provincial-level region is used as the evaluation target, measuring the region's energy efficiency only considers total energy consumption and regional GDP, making it difficult to fully reflect the region's actual energy efficiency level. Therefore, a method, storage medium, and device for evaluating total-factor energy efficiency are proposed. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the present invention proposes a method, storage medium and device for evaluating total energy utilization efficiency.

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] A method for evaluating total factor energy efficiency comprises the following steps:

[0006] Input variables, output variables and environmental variables are used as evaluation indicators of total factor energy efficiency;

[0007] The three-stage data envelopment analysis method is used to construct a total factor energy efficiency evaluation model, which includes the SBM model and the SFA model;

[0008] Substitute the data of the area to be evaluated into the total factor energy efficiency evaluation model to calculate the total factor energy utilization efficiency of the area to be evaluated.

[0009] Furthermore, the three-stage data envelopment analysis method is used to construct a full-factor energy efficiency evaluation model, which includes the following steps:

[0010] The SBM model considering undesirable output is used to analyze the decision-making units and obtain the efficiency value and input slack of each decision-making unit;

[0011] The SFA model is established with input slack as the explained variable and external environmental factors as the explanatory variables;

[0012] Based on the regression results of the SFA model, the inputs in the first stage are adjusted in terms of environmental factors and random errors, so that the decision-making units after the adjustment face the same external environment and the same luck component;

[0013] Using the adjusted inputs and original outputs from the second stage, the efficiency of each decision-making unit is recalculated through the SBM model.

[0014] Furthermore, the specific steps for constructing the SBM model are:

[0015]

[0016] Establish the optimization equation of the SBM model:

[0017] Among them, the vector s - ∈R m and They represent the redundancy of input and non-desirable output, i.e., slack variables, represents the shortage of expected output, i.e., the remaining variable; m, s1, and s2 represent the number of variables of input, expected output, and non-expected output, respectively; Represents the input, expected output, and unexpected output vector value of the kth decision-making unit, (X,Y g ,Y b ) represents the vector value of all decision-making unit inputs, expected outputs, and unexpected outputs; n represents the number of decision-making units, λ∈R n represents the weight of the decision-making unit;

[0018] Convert the optimization equation into an equivalent linear equation:

[0019]

[0020]

[0021] Find the mathematical relationship between the solutions of the linear equation and the optimization equation:

[0022] The solutions to the hypothetical fractional equation and linear equation optimization problems are The two solutions have the following relationship:

[0023]

[0024]

[0025] Substitute the input variables and output variables and solve the linear equation to obtain the efficiency value and input slack of each decision-making unit.

[0026] Furthermore, the specific steps for constructing the SFA model are:

[0027] Decomposing the slack variable into a function containing three independent variables: environmental factors, random factors, and management inefficiency, the expression is as follows:

[0028] S nk =f n (Z k β n )+V nk +U nk

[0029] n=1,2,…,N; k=n=1,2,…,K

[0030]

[0031] Among them, S nk is the slack variable of the kth decision-making unit on the nth input, that is, the difference between the ideal input and the actual input; f n (Z k β n ) is used to indicate the effect of environmental factors on S nk The impact of Z k is the observed environmental variable; β n is the parameter vector corresponding to the environmental variable; V nk +U nk is called the joint error term ε, where V nk reflects the random error and is normally distributed, that is, U nk It reflects the inefficiency of management and is a truncated normal distribution, that is, V nk with U nk Independent and uncorrelated; γ is the proportion of technical inefficiency variance to total variance.

[0032] Furthermore, based on the calculation results of the SFA model, the environmental factors and random errors of the first-stage inputs are adjusted so that the adjusted decision-making units face the same external environment and the same luck factors. The specific steps include the following:

[0033] According to the regression results of the SFA model, the input items of the decision-making unit are further adjusted, and β is calculated through maximum likelihood estimation. n , σ 2 and the estimated values of the γ parameter;

[0034] Separate the management inefficiency term and the random interference term,

[0035] The formula for management inefficiency term U is:

[0036] The formula for random interference term V is: E[Vnk |V nk +U nk ]=S nk -f n (Z k β n )-E[U nk |V nk +U nk ]

[0037] in λ=σ un / σ vn ,ε=V nk +U nk , Φ are the density function and distribution function of the standard normal distribution;

[0038] Substitute the calculated random interference term V into the formula:

[0039]

[0040] Get new input values and increase input to decision-making units with better environments or better luck.

[0041] Furthermore, input variables include labor, energy consumption, and capital stock;

[0042] Output variables include expected output and unexpected output; the unexpected output uses the entropy method to determine the weight of each part of the unexpected output and is calculated through the weighted method;

[0043] Environmental variables include economic development level, industrial structure, resource endowment and the proportion of new energy power generation.

[0044] Furthermore, the indicator for measuring the level of economic development is GDP per capita; the indicator for measuring the industrial structure is the ratio of secondary industry to GDP; and the indicator for measuring resource endowment is the number of people employed in the mining industry.

[0045] In a second aspect, the present invention further provides a storage medium storing a computer-executable program, which, when executed by a processor, is used to implement the method for evaluating total factor energy efficiency as described in any one of the above items.

[0046] In a third aspect, the present invention further provides a device for evaluating total energy efficiency, comprising:

[0047] at least one memory for storing a program;

[0048] At least one processor is configured to load the program to execute the method for evaluating total factor energy efficiency as described above.

[0049] Beneficial effects of the present invention:

[0050] The present invention provides an evaluation method for total factor energy efficiency that eliminates the influence of environmental factors. By determining the evaluation index of total factor energy efficiency, building an SBM model that takes non-expected output into consideration to obtain input slack variables, building an SFA model with input slack as the explained variable and external environmental factors as the explanatory variables, obtaining corresponding data from provinces and cities across the country, and bringing the data into the model for empirical verification, the comprehensiveness and reliability of energy efficiency evaluation are effectively improved, ensuring that energy efficiency evaluation can be carried out in the same environment in all regions. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The present invention will be further described below with reference to the accompanying drawings.

[0052] Figure 1 Flowchart of the evaluation method for total energy efficiency after eliminating the influence of environmental factors;

[0053] Figure 2 This is a structural diagram of an SBM model according to an embodiment of the present invention;

[0054] Figure 3 This is a structural diagram of an SFA model according to an embodiment of the present invention;

[0055] Figure 4 This is a diagram showing the evaluation results of total energy efficiency of provinces and cities after eliminating the impact of environmental factors according to an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0057] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0058] A method for evaluating total factor energy efficiency comprises the following steps:

[0059] Step 1: Use input variables, output variables and environmental variables as evaluation indicators of total factor energy efficiency;

[0060] Among them, input variables include labor, energy consumption, and capital stock;

[0061] Labor force: the total number of employed people in the society.

[0062] Energy consumption: Energy is divided into renewable energy and non-renewable energy. Two indicators are selected: total energy consumption and total water consumption. Total energy consumption is calculated by summing up the standard coal equivalents of major energy products using the nationally prescribed standard coal equivalent coefficients for various energy sources.

[0063] Capital stock: the total amount of capital accumulated by the economy and society over a certain period of time.

[0064] The output variable not only considers the expected output, but also the undesired output, and adopts a weighted selection of multiple pollutions. The entropy method is used to determine the weight of each part, and multiple indicators are combined into one indicator to obtain the main pollutant emissions in wastewater and the main pollutant emissions in exhaust gas, which is more comprehensive.

[0065] In terms of environmental variables, in addition to the level of economic development, industrial structure, and resource endowment, the proportion of renewable energy power generation in each region was also added;

[0066] Economic development level: Different regions have different levels of economic development. Generally speaking, with the same energy consumption, economically developed regions can create more GDP, so per capita GDP is used as an indicator to measure the level of economic development.

[0067] Industrial structure: The primary industry refers to agriculture, forestry, animal husbandry, and fishery; the secondary industry refers to mining, manufacturing, the production and supply of electricity, heat, gas, and water, and construction; the tertiary industry, or the service industry, refers to all industries other than the primary and secondary industries. The secondary industry is primarily industrial, and compared to agriculture and the service industry in the primary and tertiary industries, it consumes the most energy and contributes most to energy efficiency. Therefore, the ratio of the secondary industry to GDP is used as a measurement indicator.

[0068] Energy endowment: Considering that when measuring the resource abundance of various regions, GDP-based indicators often tend to "measure" regions with higher economic development as relatively resource-poor, which does not accurately reflect the true resource endowment, the number of people employed in the mining industry is used as an indicator to measure the abundance of natural resources in a region;

[0069] Renewable energy power generation: the proportion of hydropower, wind power, nuclear power, and solar power generation. Due to the serious lack of relevant data, and the power generation after deducting thermal power is basically new energy, the total power generation minus thermal power will be used to replace the new energy power generation, and the ratio of new energy power generation to total power generation will be used as the indicator.

[0070] Step 2: Use the three-stage data envelopment analysis method to construct a full-factor energy efficiency evaluation model, which includes the SBM model and the SFA model. The specific steps include:

[0071] The SBM model, which considers undesirable outputs, was used to analyze the decision-making units (DMUs) and obtain their efficiency and input slack. A DMU is an operational entity that can convert inputs into outputs, and each DMU has the same multiple inputs and outputs. The DMUs in this paper represent 30 provinces, autonomous regions, and municipalities directly under the central government in China.

[0072] The SFA model is established with input slack as the explained variable and external environmental factors as the explanatory variables;

[0073] Based on the regression results of the SFA model, the inputs in the first stage are adjusted in terms of environmental factors and random errors, so that the decision-making units after the adjustment face the same external environment and the same luck component;

[0074] Using the adjusted inputs and original outputs from the second stage, the efficiency of each decision-making unit is recalculated through the SBM model.

[0075] The specific steps of the SBM model considering undesirable outputs are as follows:

[0076]

[0077] Determine the optimization equation for the model:

[0078] vector s - ∈R m and They represent the redundancy of input and non-desirable output, i.e., slack variables, represents the shortage of expected output, i.e., the remaining variable. m, s1, and s2 represent the number of variables of input, expected output, and non-expected output, respectively. Represents the input, expected output, and unexpected output vector value of the kth decision-making unit, (X,Y g ,Y b ) represents the vector value of all decision-making unit inputs, expected outputs, and unexpected outputs. n represents the number of decision-making units, λ∈R n Represents the weight of the decision-making unit.

[0079] Through mathematical transformation, the fractional equation is converted into an equivalent linear equation:

[0080]

[0081]

[0082] Find the mathematical relationship between the solutions of linear equations and optimization equations: Assume that the solutions of the fractional equation and the linear optimization problem are The two solutions have the following relationship:

[0083]

[0084]

[0085] Substitute the input variables and output variables, solve the linear equation, and then obtain the preliminary energy utilization efficiency and input slack variables.

[0086] Furthermore, the specific steps of the SFA model are as follows:

[0087] Construct the SFA model and decompose the slack variable into a function containing three independent variables: environmental factors, random factors, and management inefficiency. Its expression is as follows:

[0088] S nk =f n (Z k β n )+V nk +U nk

[0089] n=1,2,…,N; k=n=1,2,…,K

[0090]

[0091] Among them, S nk is the slack variable of the kth decision-making unit on the nth input, that is, the difference between the ideal input and the actual input; f n (Z k β n ) is used to indicate the effect of environmental factors on S nk The influence of f n (Z k β n )=Z k β n , Z k is the observed environmental variable, β n is the parameter vector corresponding to the environmental variable; V nk +U nk is called the joint error term ε, where V nk reflects the random error and is normally distributed, that is, U nk It reflects the inefficiency of management and is a truncated normal distribution, that is, Generally speaking, μ u =0, U nk>0. V nk with U nk Independent and uncorrelated. γ is the proportion of technical inefficiency variance to total variance. When the value of γ approaches 1, the influence of management inefficiency dominates; when the value of γ approaches 0, the influence of random errors dominates.

[0092] Using the regression results of the SFA model, the input items of the decision-making unit are further adjusted, and β is calculated by maximum likelihood estimation using software such as Frontier4.1. n , σ 2 and the estimated values of the γ parameter;

[0093] Separate the management inefficiency term and the random interference term,

[0094] The formula for management inefficiency term U is:

[0095] The formula for random interference term V is: E[V nk |V nk +U nk ]=S nk -f n (Z k β n )-E[U nk |V nk +U nk ]

[0096] in λ=σ un / σ vn ,ε=V nk +U nk , Φ are the density function and distribution function of the standard normal distribution respectively.

[0097] Substitute the calculated random interference term V into the formula:

[0098]

[0099] Get the new input value. The first bracket adjusts the influence of environmental factors. max(Z k β n ) represents the worst environmental condition, and other decision-making units are adjusted based on it. k β n The smaller the value, the better the condition. The more investment, the better the condition. k β nThe larger the value, the worse the conditions, and the less input is added, so that all decision-making units are adjusted to the same environmental level; the second bracket adjusts the random error factor, and the principle is the same as above, even if all decision-making units face the same luck.

[0100] The corrected input value is brought back into the SBM model to obtain the evaluation result of total factor energy efficiency excluding the influence of environmental factors.

[0101] Step 3: Substitute the data of the area to be evaluated into the total factor energy efficiency evaluation model to calculate the total factor energy utilization efficiency of the area to be evaluated.

[0102] Substitute the data of each province and city into the evaluation method of total energy efficiency of the present invention for evaluation, such as Figure 2 As shown, in this embodiment, the specific steps of the SBM model considering undesirable output are as follows:

[0103] Step 1: Determine the optimization equation of the SBM model;

[0104] Step 2: Convert the optimization equation in the form of a fractional equation into an equivalent linear equation through mathematical transformation;

[0105] Step 3: Find the mathematical relationship between the linear equation and the solution of the optimization equation;

[0106] Step 4: Process the required input variables and output variables;

[0107] Step 5: Substitute the processed variables into the model and solve the linear equation;

[0108] Step 6: Convert the solution of the linear equation into the solution of the optimization equation to obtain the preliminary energy utilization efficiency, input and output slack variables.

[0109] like Figure 3 As shown, in this embodiment, the specific steps of the SFA model are as follows:

[0110] Step 1: Construct an SFA model and decompose the slack variables obtained from the SBM model into a function containing three independent variables: environmental factors, random factors, and management inefficiency;

[0111] Step 2: Use frontier4.1 to perform regression to test whether there is management inefficiency in the input slack variables;

[0112] Step 3: Calculate the estimated values of relevant parameters through maximum likelihood estimation;

[0113] Step 4: Separate the management inefficiency term and the random interference term;

[0114] Step 5: Increase the input for decision-making units that are in a better environment or have better luck, substitute the new input value into the formula to calculate the new input value under the homogeneous environment, and then bring it into the SBM model again to obtain the evaluation result of total factor energy utilization efficiency after eliminating the influence of environmental factors.

[0115] Figure 4 This graph shows the evaluation results of total energy efficiency for provinces and cities, eliminating the influence of environmental factors, according to an embodiment of the present invention. Compared with single-factor energy efficiency, it more accurately assesses energy utilization in each province and city. This graph reflects my country's energy efficiency and is consistent with the results of the total energy efficiency evaluation method of the present invention.

[0116] The embodiment of the present invention also discloses a device for evaluating the total energy efficiency, which is used to run a database stored procedure, wherein the above-mentioned Figure 1 、 Figure 2 、 Figure 3 and Figure 4 A method for evaluating total factor energy efficiency is disclosed.

[0117] The embodiment of the present invention also discloses a computer storage medium, wherein the storage medium includes a storage database storage process, wherein when the database storage process is running, the device where the storage medium is located is controlled to execute the above Figure 1 、 Figure 2 、 Figure 3 and Figure 4 A method for evaluating total factor energy efficiency is disclosed.

[0118] In the context of the present disclosure, computer storage media can be tangible media that can contain or store programs for use by or in combination with an instruction execution system, device or equipment. Machine-readable media can be machine-readable signal media or machine-readable storage media. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0119] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the invention as claimed.

Claims

1. A method for evaluating total factor energy efficiency, characterized in that: The following steps are involved: Input variables, output variables, and environmental variables are used as evaluation indicators for total factor energy efficiency. The input variables include energy consumption, which includes two indicators: total energy consumption and total water consumption. The environmental variables include the proportion of renewable energy power generation in each region. The three-stage data envelopment analysis method is used to construct a total factor energy efficiency evaluation model, which includes the SBM model and the SFA model; Substitute the data of the area to be evaluated into the total factor energy efficiency evaluation model to calculate the total factor energy utilization efficiency of the area to be evaluated; The construction of a full-factor energy efficiency evaluation model using the three-stage data envelopment analysis method includes the following steps: The SBM model considering undesirable output is used to analyze the decision-making units and obtain the efficiency value and input slack of each decision-making unit; The SFA model is established with input slack as the explained variable and external environmental factors as the explanatory variables; Based on the regression results of the SFA model, the inputs in the first stage are adjusted in terms of environmental factors and random errors, so that the decision-making units after the adjustment face the same external environment and the same luck component; Using the adjusted inputs and original outputs from the second stage, the efficiency of each decision-making unit is recalculated through the SBM model; The specific steps of constructing the SBM model are: Establish the optimization equation of the SBM model: Among them, the vector s - ∈R m and They represent the redundancy of input and non-desirable output, i.e., slack variables, represents the shortage of expected output, i.e., the remaining variable; m, s1, and s2 represent the number of variables of input, expected output, and non-expected output, respectively; Represents the input, expected output, and unexpected output vector value of the kth decision-making unit, (X,Y g ,Y b ) represents the vector value of all decision-making unit inputs, expected outputs, and unexpected outputs; n represents the number of decision-making units, λ∈R n represents the weight of the decision-making unit; Convert the optimization equation into an equivalent linear equation: Find the mathematical relationship between the solutions of the linear equation and the optimization equation: The solutions to the hypothetical fractional equation and linear equation optimization problems are The two solutions have the following relationship: Substitute the input variables and output variables and solve the linear equation to obtain the efficiency value and input slack of each decision-making unit; The specific steps of constructing the SFA model are: Decomposing the slack variable into a function containing three independent variables: environmental factors, random factors, and management inefficiency, the expression is as follows: S nk =f n (Z k ;β n )+V nk +U nk n=1,2,…,N; k=n=1,2,…,K Among them, S nk is the slack variable of the kth decision-making unit on the nth input, that is, the difference between the ideal input and the actual input; f n (Z k β n ) is used to indicate the effect of environmental factors on S nk The impact of Z k is the observed environmental variable; β n is the parameter vector corresponding to the environmental variable; V nk +U nk is called the joint error term ε, where V nk reflects the random error and is normally distributed, that is, U nk It reflects the inefficiency of management and is a truncated normal distribution, that is, V nk with U nk Independent and uncorrelated; γ is the proportion of technical inefficiency variance to total variance.

2. The method for evaluating total energy efficiency according to claim 1, wherein: Based on the results of the SFA model, the environmental factors and random errors of the first-stage inputs are adjusted so that the adjusted decision-making units face the same external environment and the same luck factors. The specific steps include the following: According to the regression results of the SFA model, the input items of the decision-making unit are further adjusted, and β is calculated through maximum likelihood estimation. n , σ 2 and the estimated values of the γ parameter; Separate the management inefficiency term and the random interference term, The formula for management inefficiency term U is: The formula for random interference term V is: E[V nk |V nk +U nk ]=S nk -f n (Z k β n )-E[U nk |V nk +U nk ] in λ=σ un / σ vn ,ε=V nk +U nk , Φ are the density function and distribution function of the standard normal distribution; Substitute the calculated random interference term V into the formula: n=1,2,…,N; k=n=1,2,…,K Get new input values and increase input to decision-making units with better environments or better luck.

3. The method for evaluating total energy efficiency according to claim 1, wherein: Input variables include labor, energy consumption, and capital stock; Output variables include expected output and unexpected output; the unexpected output uses the entropy method to determine the weight of each part of the unexpected output and is calculated through the weighted method; Environmental variables include economic development level, industrial structure, resource endowment and the proportion of new energy power generation.

4. The method for evaluating total energy efficiency according to claim 3, wherein: The indicator for measuring economic development level is GDP per capita; the indicator for measuring industrial structure is the ratio of secondary industry to GDP; and the indicator for measuring resource endowment is the number of people employed in the mining industry.

5. A storage medium, characterized in that A computer-executable program is stored therein, and when the computer-executable program is executed by a processor, it is used to implement the evaluation method for total factor energy utilization efficiency as described in any one of claims 1 to 4.

6. A device for evaluating total energy efficiency, characterized in that ,include: at least one memory for storing a program; At least one processor is used to load the program to execute the method for evaluating total factor energy efficiency according to any one of claims 1 to 4.

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