A method and system for constructing a comprehensive energy consumption evaluation index and an electrification comprehensive index

Abstract

This invention belongs to the field of index construction technology, and particularly relates to a method for constructing a comprehensive energy consumption evaluation index and an electrification comprehensive index. This method is based on a systems framework of dual-carbon objectives, taking into account both energy consumption and carbon emissions. Its core innovation lies in constructing a multi-dimensional collaborative measurement system integrating total scale, economic efficiency, structural optimization, and dynamic trends. This index construction system realizes multi-level subdivision and application of indicators, constructing a comprehensive evaluation framework covering seven secondary indicators from four dimensions: total scale, economic efficiency, structural transformation, and dynamic trends. At the basic accounting level, the total scale dimension uses two absolute indicators: total energy consumption (TEC, unit: tce) and total carbon emissions (TCE, unit: tCO2), reflecting the total scale of regional terminal energy consumption converted to standard coal equivalent and the total carbon dioxide emission load, respectively.

Description

Technical Field

[0001] This invention belongs to, but is not limited to, the field of index construction technology, and particularly relates to a method and system for constructing a comprehensive energy consumption evaluation index and an electrification comprehensive index. Background Technology

[0002] Against the backdrop of accelerated global energy transition and the in-depth implementation of "dual carbon" targets, scientifically measuring regional energy consumption and electrification development levels has become a key path to promote energy structure optimization and facilitate the green and low-carbon transformation of the economy and society. Currently, both domestically and internationally, energy consumption assessment and electrification evaluation are evolving from single-dimensional to multi-dimensional collaboration, and from static accounting to dynamic monitoring. In terms of energy consumption assessment, China has constructed an assessment framework based on the "General Rules for Comprehensive Energy Consumption Calculation," encompassing total energy consumption, energy consumption per unit of GDP, energy consumption per unit of product, and a comprehensive energy evaluation system. It widely applies multi-dimensional comprehensive evaluation methods such as fuzzy evaluation and coupling coordination degree analysis. The United States, Japan, and the European Union, among others, have established models such as hierarchical energy intensity indicators, building energy asset scoring, BEI values ​​and the "Top Runner" program, energy efficiency indices, and life-cycle assessments. In terms of electrification assessment, China has established a multi-level evaluation system covering the national, provincial, municipal, key scenarios, and industrial park factories, with core indicators such as terminal electrification rate, the proportion of non-fossil fuel electricity, and comprehensive line loss rate. Countries such as the United States, Norway, and Japan have constructed their own unique assessment frameworks for electrification penetration, power supply reliability, and environmental benefits, based on energy independence, clean energy endowment, and resource constraints. Overall, energy consumption and electrification assessment technologies are continuously developing towards systematization, refinement, and dynamism, providing important theoretical support and quantitative tools for regional energy planning and low-carbon policy formulation.

[0003] Although existing assessment technologies have a certain foundation, there are still many technical limitations closely related to model construction when adapting to the needs of precise regional assessment and differentiated policy implementation under the "dual carbon" target. In the field of energy consumption assessment, the dynamism of existing models is significantly insufficient, making it difficult to reflect the dynamic changes and medium- and long-term trends of energy consumption in a timely and accurate manner; the availability, accuracy, and completeness of energy consumption data are prominent issues, and there are data barriers for refined assessments across years and regions. Moreover, the assessment dimensions are mostly concentrated on total amount and intensity, and there is insufficient comprehensive consideration of carbon emission performance, energy structure optimization, and dynamic evolution trends; some complex assessment methods involve a large amount of calculation and professional analysis, which are not practical and difficult to promote at the grassroots level. At the same time, traditional subjective weighting methods are easily affected by human factors, leading to biased evaluation results. In the field of electrification assessment, existing indicator systems mostly focus on outcome indicators such as electrification rate, while failing to adequately consider key dimensions such as power system operating efficiency, renewable energy absorption capacity, regional energy endowment suitability, and economic viability. Cross-departmental and cross-industry data collaboration efficiency needs improvement, and inconsistent energy data collection standards in some grassroots scenarios and SMEs hinder the accurate identification of regional differences. Existing models primarily rely on static calculations and current status assessments, and customized assessment modules for different regional industrial structures and energy conditions are relatively lacking. These technical bottlenecks directly restrict the practical effectiveness of energy consumption and electrification assessments in formulating regionally differentiated policies, precise energy-saving regulation, and implementing "dual control" targets. There is an urgent need to construct a comprehensive assessment index that combines objective weighting, multi-dimensional coverage, dynamic trend analysis, and scenario adaptability. Summary of the Invention

[0004] To address the aforementioned technical problems, the present invention aims to provide a method for constructing a comprehensive energy consumption evaluation index and a comprehensive electrification index.

[0005] The present invention is implemented as follows: a comprehensive energy consumption evaluation index construction system, characterized in that the system includes a multi-dimensional collaborative measurement framework generation unit and a comprehensive index calculation unit;

[0006] The multidimensional collaborative measurement framework generation unit is used to construct a measurement system that includes the total scale dimension, economic efficiency dimension, structural transformation dimension, and dynamic trend dimension. Among them, the total scale dimension is configured with total energy consumption index and total carbon emission index, the economic efficiency dimension is configured with energy consumption per unit of GDP index and carbon emission per unit of GDP index, the structural transformation dimension is configured with clean energy ratio index, and the dynamic trend dimension is configured with energy consumption change rate per unit of GDP index and energy consumption elasticity coefficient index.

[0007] The comprehensive index calculation unit is used to combine the standardized data of the above indicators with the objective weights determined based on the entropy weight method to calculate the comprehensive energy consumption evaluation index.

[0008] Furthermore, the system also includes an index calculation unit for performing the following calculations:

[0009] The total energy consumption index is calculated by multiplying the physical quantity of the final consumption of the i-th type of energy by the conversion factor of the i-th type of energy, and then summing the results over all energy types.

[0010] The total carbon emission index is calculated by multiplying the physical amount of final consumption of the i-th energy source by the carbon dioxide emission factor of the i-th energy source, and then summing the results over all energy types.

[0011] The energy consumption per unit of GDP is calculated as follows: multiply the physical quantity of various types of final energy in period t by the corresponding standard coal equivalent coefficient and sum them up, then divide by the regional GDP at constant prices in the same period.

[0012] The carbon emission index per unit of GDP is calculated by dividing the total carbon emissions in year t by the regional GDP at constant prices in the same period.

[0013] The clean energy share indicator is calculated by dividing clean energy consumption by total energy consumption in the same period.

[0014] The calculation method for the rate of change in energy consumption per unit of GDP is as follows: divide the difference between the energy consumption per unit of GDP in period t and the previous period by the energy consumption per unit of GDP in the previous period.

[0015] The energy consumption elasticity coefficient is calculated by dividing the rate of change in energy consumption by the rate of change in GDP at constant prices.

[0016] Furthermore, the system also includes a data preprocessing unit for performing the following steps:

[0017] Energy data is obtained by acquiring the end-use physical consumption of multiple energy types based on the energy balance sheet;

[0018] Extract the annual GDP and industrial added value of each region as economic data;

[0019] The physical quantities of each energy type are uniformly converted into standard coal equivalents.

[0020] Furthermore, the composite index calculation unit is also used to perform the following steps:

[0021] The range of each indicator was standardized.

[0022] Calculate the characteristic weight of each evaluation object on each indicator;

[0023] The entropy value of each indicator is calculated based on the feature weight;

[0024] Calculate the redundancy of each indicator based on the entropy value;

[0025] The weights of each indicator are calculated based on redundancy.

[0026] Construct a weighted matrix;

[0027] Determine the optimal and worst solutions based on the weighted matrix;

[0028] Calculate the Euclidean distance between each evaluation object and the optimal and worst solutions;

[0029] The relative proximity is calculated based on the Euclidean distance, and this relative proximity is used as the comprehensive energy consumption evaluation index.

[0030] This invention also provides a method for constructing a comprehensive electrification index. For multiple electrification indicators in the electrification evaluation system, the entropy weight method is used to determine the objective weight of each indicator, and the relative proximity of each evaluation object is calculated based on the TOPSIS method. This relative proximity is used as the comprehensive electrification score to evaluate the level of electrification development.

[0031] Furthermore, the method also includes: standardizing multiple electrification indicators; calculating the characteristic weight of each evaluation object on each electrification indicator, wherein the characteristic weight is the standardized value of a certain indicator divided by the sum of all standardized values ​​of that indicator.

[0032] Furthermore, the method also includes: calculating the entropy value of each electrification index based on the characteristic weight of each index, which reflects the degree of variation of each index.

[0033] Furthermore, the method also includes: calculating the redundancy of each electrification index based on the entropy value of each index, where the redundancy is the information utility value.

[0034] Furthermore, the method also includes: calculating the weight of each electrification index based on the redundancy of each index, wherein the weight is determined by the proportion of the redundancy of the index to the total redundancy of all indices.

[0035] Furthermore, the method also includes: multiplying the standardized value of each electrification indicator by its corresponding weight to obtain the individual score of the indicator; and summing up the individual scores of all electrification indicators of the evaluation object to obtain the comprehensive electrification score.

[0036] Based on the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solution to be protected by this invention are as follows:

[0037] This invention constructs a regional comprehensive energy consumption evaluation index based on dual carbon targets, encompassing four dimensions: total scale, economic efficiency, structural transformation, and dynamic trends. It also develops a comprehensive electrification index covering three dimensions: electricity consumption, electricity supply, and electricity efficiency. This invention belongs to the field of energy economic assessment technology. The advantages of this technology are detailed below:

[0038] (1) Constructing a comprehensive energy consumption evaluation index to achieve a leap from a single dimension to a systematic assessment. This technology breaks through the traditional two-dimensional framework of total amount + intensity, and innovatively incorporates total scale, economic efficiency, structural transformation, and dynamic trends into a unified evaluation logic. Among them, the introduction of total carbon emissions and carbon emissions per unit of GDP enables energy consumption assessment to be directly linked to the dual carbon targets; the clean energy proportion indicator fills the gap in the traditional assessment of the effectiveness of clean energy structure optimization; and the dynamic trend indicator realizes the extension from static profile to dynamic monitoring. Thus, the assessment results can not only reflect the current level of regional energy consumption, but also provide a scientific basis for the dynamic adjustment of energy policies.

[0039] (2) Construct a three-dimensional electrification evaluation index encompassing electricity consumption, electricity supply, and electricity energy efficiency, breaking through the limitations of a single electrification rate. While retaining the fundamental value of the terminal electrification rate, this technology expands the evaluation perspective to include clean power supply (proportion of non-fossil energy power generation, penetration rate of new energy sources) and electricity energy efficiency (electricity consumption per unit of GDP, electricity consumption elasticity coefficient). This assessment method accurately measures the level of regional electrification development, characterizes development features, and analyzes evolution trends, providing quantitative basis and decision-making support for high-quality electrification development and the implementation of dual-carbon goals in Central China and even nationwide.

[0040] (3) The TOPSIS entropy weighting method is used for objective weighting, which significantly improves the scientificity and credibility of the evaluation. This technology abandons the traditional subjective weighting method and innovatively introduces the information entropy theory: the smaller the entropy value, the greater the dispersion of the indicator among different evaluation objects and the richer the information contained, and the higher the weight is assigned; conversely, the higher the entropy value, the lower the weight is assigned. This process is completely data-driven and does not require expert scoring, thus avoiding interference from human preferences. Combined with the ideal solution approximation mechanism of the TOPSIS method, by calculating the Euclidean distance and relative proximity between the evaluation object and the positive and negative ideal solutions, the multidimensional indicator information is condensed into a comprehensive index in the [0,1] interval, realizing the accurate decoding and intuitive expression of complex data structures.

[0041] (4) Range standardization and constant price conversion methods are introduced to standardize the range of indicators with different dimensions (such as total energy consumption, energy consumption per unit of GDP, and per capita electricity consumption) and map them to the [0,1] interval; for value indicators involving GDP, the constant price conversion based on the 2014 base period is uniformly adopted to eliminate the interference of macroeconomic policies and market price fluctuations. Therefore, the average index results have strict longitudinal comparability (different periods in the same region) and horizontal comparability (different regions in the same period).

[0042] (5) To achieve refined assessment by industry and sector, this technology subdivides the core indicators into three major industries and six sub-sectors (agriculture, forestry, animal husbandry and fishery; industry; construction; transportation, warehousing and postal services; wholesale, retail, accommodation and catering; and other industries) according to the National Economic Industry Classification. Panel data are established for calculation in four provinces in Central China (Henan, Hubei, Hunan and Jiangxi). This design can reveal the systemic differences in production methods and energy dependence among different industries, accurately locate high-energy-consuming industries and energy-efficient low-efficiency areas, and provide data support for the formulation of differentiated industrial energy-saving policies.

[0043] (6) The index construction has good flexibility and scalability, and can adapt to the assessment needs of multiple scenarios. The index framework and calculation process of this technology adopt a standardized design, and the index composition and calculation parameters can be flexibly adjusted according to different assessment objects (province, city, park, industry). The general architecture of the model ensures that it can be widely used in energy consumption and electrification assessment in different regions, industries and time scales, and meet the diversified and refined governance needs under the dual carbon target. Attached Figure Description

[0044] Figure 1 This is a flowchart illustrating the construction of a comprehensive energy consumption evaluation index and a comprehensive electrification index. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0046] like Figure 1 As shown, this embodiment of the invention provides a method for constructing a comprehensive energy consumption evaluation index. The method is based on the systems theory framework of dual carbon objectives, taking into account both energy consumption and energy carbon emissions. Its core innovation lies in constructing a multi-dimensional collaborative measurement system that integrates total scale, economic efficiency, structural optimization and dynamic trends.

[0047]

[0048] The index construction system realizes the multi-level subdivision and application of indicators. It constructs a comprehensive evaluation framework covering 7 secondary indicators from four dimensions: total scale, economic efficiency, structural transformation and dynamic trend. At the basic accounting level, the total scale dimension reflects the total scale of regional terminal energy consumption in terms of standard coal equivalent and the total carbon dioxide emission load through two absolute indicators: total energy consumption TEC (tce) and total carbon emissions TCE (tCO2).

[0049] The economic efficiency dimension introduces two intensity indicators: energy consumption per unit of GDP (EUI) and carbon emissions per unit of GDP (CEF). By analyzing the ratio of energy consumption and carbon emissions to regional GDP, it reflects the level of energy consumption and the status of energy conservation and emission reduction. In addition, the structural transformation dimension uses the proportion of clean energy (CER) as the core indicator to quantify the proportion of clean energy in final consumption. The dynamic trend dimension uses two forward-looking indicators: the rate of change of energy consumption per unit of GDP (ΔEUI) and the energy elasticity coefficient (EEC) to capture the relative change in energy consumption between adjacent accounting periods and the ratio of energy consumption growth rate to economic growth rate, providing a dynamic monitoring tool.

[0050] (1) Total scale - Total energy consumption

[0051] Total energy consumption refers to the sum of all types of energy consumed by all industries and households nationwide within a certain period. It is a core indicator for measuring the overall level of regional energy consumption. The formula for calculating this indicator is:

[0052]

[0053] Among them, Total energy consumption This represents the physical quantity of the i-th energy source in final consumption. represents the conversion factor for the i-th energy source (used to convert the consumption of different energy sources into a unified energy unit—standard coal), and n represents the number of energy types. This indicator directly reflects the total consumption of various energy sources in a region within a certain period. Because different energy sources such as coal, oil, natural gas, and electricity have different physical forms and energy densities, they cannot be directly added together. The conversion factor unifies all types of energy into standard coal units, allowing the total energy consumption to directly reflect the total consumption of various energy sources in a region within a certain period, providing a unified benchmark for subsequent comparative analyses.

[0054] (2) Total scale - Total carbon emissions

[0055] Total carbon emissions are a core extension indicator of the overall scale dimension, focusing on the direct environmental impact of energy consumption. Its selection criteria closely align with the dual-carbon goal orientation, breaking through the limitation of focusing solely on quantity in total energy consumption. It achieves a dual consideration of both the quality and quantity of energy consumption, linking the total scale of energy consumption while focusing on the environmental externalities of carbon emissions caused by energy consumption. The calculation formula for this indicator is:

[0056]

[0057] TCE represents total carbon emissions. Let i be the physical quantity of the final consumption of the i-th energy source. Let be the carbon dioxide emission factor for the i-th energy source, and n be the number of energy types. This indicator measures the absolute amount of direct carbon dioxide emissions generated by regional final energy consumption. Since different energy types have different carbon content and combustion efficiencies, their carbon dioxide emissions per unit consumption also differ. By selecting authoritative emission factors, the scientific accuracy and reliability of total carbon emission accounting are ensured.

[0058] (3) Economic efficiency - energy consumption per unit of GDP

[0059] Energy intensity, also known as energy consumption per unit of GDP, refers to the ratio of a country's or region's total energy consumption to its GDP over a specific period. It primarily reflects the degree to which economic growth depends on energy consumption and is a core efficiency indicator for measuring regional energy utilization efficiency and the effectiveness of energy conservation and emission reduction. The formula for calculating this indicator is:

[0060]

[0061] Where t represents the period; For the physical quantity of the i-th type of terminal energy; The standard coal equivalent coefficient for energy type i; This represents the region's GDP at constant prices for the current period. Higher energy consumption per unit of GDP indicates a greater dependence of economic development on energy.

[0062] (4) Economic efficiency - carbon emissions per unit of GDP

[0063] Carbon intensity, also known as carbon emissions per unit of GDP, is the ratio of a country's or region's total carbon emissions to its GDP over a specific period. It is a core efficiency indicator linking total carbon emissions to economic development, and its calculation formula is as follows:

[0064]

[0065] in, Let be the carbon emissions per unit of GDP in year t. Let be the total carbon emissions in year t. Let be the regional GDP at constant prices in year t. This indicator is a key strength indicator for measuring the level of regional economic decarbonization and carbon emission reduction efficiency, directly corresponding to the carbon emission intensity reduction requirement in the dual-carbon goals. The smaller this indicator, the lower the dependence of economic growth on carbon emissions, the higher the level of low-carbon development, and it also means that the region has effectively controlled carbon emission growth while achieving economic growth, thus achieving synergy between low-carbon development and growth.

[0066] (5) Structural transformation - clean energy level

[0067] Clean energy level, expressed as the proportion of clean energy consumption, refers to the ratio of a country's or region's clean energy consumption to its total energy consumption during a specific period. It primarily reflects the degree of greening in a region's energy consumption structure and is a core structural indicator linking energy consumption, energy efficiency, and carbon emission reduction. The formula for calculating this indicator is:

[0068]

[0069] The Clean Energy Ratio (CER) represents the proportion of clean energy in total energy consumption, specifically the share of natural gas, electricity, and other energy sources. An increase in the CER indicates that the region's energy structure is successfully transitioning towards a low-carbon model.

[0070] (6) Dynamic trend - rate of change in energy consumption per unit of GDP

[0071] The rate of change in energy consumption per unit of GDP is a core indicator for measuring the dynamic evolution of regional energy efficiency. It focuses on the increase or decrease and trend of energy consumption per unit of GDP in different periods, and fundamentally reflects the progress and effectiveness of regional energy conservation and emission reduction. The calculation formula for this indicator is as follows:

[0072]

[0073] in, It is the rate of change in energy consumption per unit of GDP. Let t be the energy consumption per unit of GDP in period t. This indicator represents the relative change in energy consumption per unit of GDP between two adjacent periods, and is used to measure the rate of improvement in static efficiency. < 0 indicates a decrease in energy intensity and an increase in efficiency, meaning that the region's energy utilization efficiency has been continuously improved during the economic development process, and the energy consumption per unit of economic output has been continuously reduced. A value greater than 0 indicates an increase in energy intensity and a decrease in efficiency, suggesting that regional energy utilization efficiency urgently needs improvement and that there may be problems such as the expansion of energy-intensive industries and energy waste, requiring timely adjustments to development strategies.

[0074] (7) Dynamic trend - energy consumption elasticity

[0075] The energy consumption elasticity coefficient is another core indicator in the dynamic trend dimension, focusing on the synergistic relationship between energy consumption and economic growth. The formula for calculating this indicator is:

[0076]

[0077] in, Let be the energy consumption elasticity coefficient for period t. For the rate of change in energy consumption, This represents the rate of change in regional GDP at constant prices. When this coefficient is greater than 1, it means that energy consumption is growing faster than the economy; when the coefficient equals 1, it indicates that energy consumption and economic growth are in sync; and when the coefficient is less than 1, it means that energy consumption is growing slower than the economy, and energy efficiency is improving.

[0078] 2. Data Preprocessing

[0079] Regarding physical energy consumption data, the final physical consumption of 30 energy types, including coal, oil, natural gas, electricity, and heat, is directly taken from the Regional Energy Balance Sheet (Physical Quantity) chapter of the *China Energy Statistical Yearbook*. Simultaneously, economic data such as the GDP of each province and the added value of the primary, secondary, and tertiary industries are uniformly taken from the *China Statistical Yearbook*. To meet the needs of energy consumption assessment, in addition to compiling the annual GDP data of each province, the added value data of each industry was also extracted simultaneously for analyzing energy intensity and energy consumption structure characteristics at different industry and sector levels. Because the units of measurement for different energy types differ (e.g., coal is measured in tens of thousands of tons, electricity in hundreds of millions of kilowatt-hours, and natural gas in hundreds of millions of cubic meters), direct aggregation and calculation are not possible. Therefore, the physical quantities of all energy types need to be uniformly converted to standard coal equivalent to ensure comparability and additivity among different energy types. The conversion process strictly follows the current national technical specifications and adopts the electrical heat equivalent to standard coal coefficient published in the "General Rules for Calculation of Comprehensive Energy Consumption" (GB / T 2589—2020). This is a basic national standard in the field of comprehensive energy consumption accounting in my country, which clearly stipulates the calculation methods and coefficient values ​​for various energy sources (coal, oil, natural gas, electricity, heat, etc.) converted to standard coal. Ultimately, it obtains unified energy consumption data measured in standard coal equivalent, providing standardized data for the calculation of comprehensive energy consumption assessment index.

[0080] In order to comprehensively and systematically analyze energy consumption varieties based on dual carbon targets, and based on existing research, this study analyzes the processing and conversion processes and end-use consumption methods of coal, oil and natural gas, and electricity item by item according to the energy variety processing and conversion processes and end-use consumption methods (referring to China's "Energy Balance Sheet"), to obtain the energy consumption varieties that need to be accounted for at the energy consumption end.

[0081] 3. Calculate the comprehensive index using entropy-weighted TOPSIS.

[0082] (1) Standardization of indicators

[0083] Range standardization is used to eliminate differences in the dimensions and orders of magnitude of the indicators. For positive indicators, larger values ​​are better; for negative indicators, smaller values ​​are better. The standardization formula is:

[0084]

[0085] (2) Calculate the proportion of the j-th index of the i-th ordinal parameter. :

[0086]

[0087] After standardization is completed, the characteristic weight of the i-th evaluation object on the j-th indicator needs to be calculated. This proportion reflects the relative contribution of each year or region to a specific indicator, which is equivalent to normalizing the standardized value.

[0088] (3) Calculate the entropy value of the j-th index:

[0089]

[0090]

[0091] Information entropy is a core indicator in information theory for measuring the degree of disorder in data: the smaller the difference in the values ​​of various evaluation objects on a certain indicator, the higher the entropy value. The larger the entropy value, the less information the indicator provides and the weaker its discriminative ability; conversely, the greater the data difference, the smaller the entropy value, and the richer the information content. This characteristic provides a theoretical basis for objective weighting—the smaller the entropy value of an indicator, the greater its degree of variation, and the more significant its discriminative effect on comprehensive evaluation.

[0092] (4) Based on the entropy value Calculate the redundancy of the j-th indicator. :

[0093]

[0094] Redundancy is inversely related to information entropy; it directly measures the dispersion and discriminative power of indicator values: when When it is small (close to 0), A value close to 1 indicates that the indicator shows significant differences among different evaluation objects, providing rich and effective information for decision-making; when... When it is relatively large (close to 1), A value close to 0 indicates that the evaluation subjects perform similarly on this indicator, resulting in low information value.

[0095] (5) Calculate the weight of the j-th indicator based on the information entropy redundancy:

[0096]

[0097] (6) Construct the weighted matrix R of the comprehensive energy consumption evaluation index:

[0098]

[0099] =

[0100] This matrix incorporates the varying importance of each indicator into the evaluation framework by multiplying the standardized indicator values ​​by their corresponding entropy weights. In the weighted matrix, elements with larger values ​​reflect both superior performance of the original data and the higher weight of the indicator, thus forming a comprehensive information carrier that balances both "performance" and "indicator importance." This matrix provides structured data support for subsequent determination of the ideal solution and distance calculation.

[0101] (7) Determine the optimal solution based on the weighting matrix. worst-case scenario :

[0102] =( , , ..., )

[0103] =( , , ..., )

[0104] Determine the optimal solution vector based on the weighted matrix. with the worst solution vector ,in = ( , , ..., ) represents the ideal solution. =( , , ..., The positive ideal solution represents the theoretically optimal state of energy consumption (such as lowest energy consumption, highest efficiency, and optimal structure), while the negative ideal solution represents the worst-case scenario. This dual reference system provides a clear benchmark for quantitatively evaluating the quality of energy consumption in different years or regions.

[0105] (8) Calculate the Euclidean distance and :

[0106] =

[0107] =

[0108] in The smaller the value, the closer the evaluation object is to the optimal solution; the smaller the value, the closer it is to the ideal state. The distance to the worst-case scenario is measured by the numerical value, indicating that the distance is further from the worst-case state. The calculation of Euclidean distance comprehensively considers the weighted differences of all dimensions. Through spatial geometric distance, it condenses multi-dimensional index information into two one-dimensional distance measures, intuitively reflecting the relative position of each evaluation object in the multi-dimensional energy consumption space.

[0109] (9) Calculate the relative closeness of each measurement scheme to the ideal scheme. :

[0110]

[0111] This index comprehensively considers the relative distance between the evaluation object and the positive and negative ideal solutions, and its value range is [0,1]: when When it is close to 1, it indicates Smaller and A relatively large value indicates that the evaluated object is very close to the optimal solution and far from the worst solution, indicating a relatively good overall energy consumption situation; when A value close to 0 indicates that the evaluated object is close to the worst-case scenario and far from the optimal scenario, indicating poor energy consumption. By ranking the relative proximity, the energy consumption of different years or regions can be ranked, and the resulting comprehensive energy consumption evaluation index provides data reference for identifying energy efficiency shortcomings and formulating differentiated energy-saving policies.

[0112] 4. Industry-specific and sector-specific calculation methods

[0113] To achieve a more refined assessment, this technology decomposes and calculates the above seven core indicators according to the "National Industrial Classification of Economic Activities" (GB / T4754-2017).

[0114] This invention provides a method for constructing a comprehensive electrification index, the method comprising:

[0115] The standardization process is illustrated by selecting the positive indicator, the proportion of electricity in final energy consumption. Standardization aims to eliminate the influence of different dimensions and orders of magnitude of indicators, making them comparable. For positive indicators, the standardized value is obtained by dividing the difference between the original value and the minimum value of the indicator across all samples by the range. For negative indicators, the standardized value is obtained by dividing the difference between the maximum value and the original value by the range. Standardized data for all other years and provinces are calculated using the same method. Through standardization, the values ​​of each indicator are mapped to... The interval effectively eliminated the difference in dimensions, providing a reliable data foundation for subsequent weight calculation based on the entropy method and measurement of the comprehensive electrification index;

[0116] 1. Construction of Electrification Index

[0117] Based on a comprehensive consideration of the inherent logic and practical needs of electrification development, and adhering to the principles of scientific rigor, systematic approach, comprehensiveness, and applicability, this paper constructs a comprehensive electrification index using 13 indicators across three dimensions: electricity consumption, electricity supply, and electricity efficiency. The aim is to provide a powerful quantitative tool for in-depth research into electrification development. The electricity consumption dimension focuses on end-user energy consumption, reflecting the breadth and depth of electrification penetration, while also considering the overall scale of electrification, reflecting the level of electricity consumption across society and its penetration in urban and rural areas. The electricity supply dimension focuses on the source production stage, determining the supporting capacity and sustainability of electrification. The electricity efficiency dimension spans the entire production and consumption chain, measuring the quality and effectiveness of electrification. These three dimensions interact and together constitute a comprehensive assessment of the scale, structure, and efficiency of electrification, providing a systematic basis for formulating scientific electrification development strategies. The specific indicator design is shown in the table below:

[0118]

[0119] The electricity consumption dimension focuses on the transformation of end-use energy structure and the effectiveness of electricity substitution. Among these, the proportion of electricity in end-use energy consumption, i.e., the electrification rate, is the most crucial structural indicator for measuring electrification, reflecting the overall degree to which electricity has replaced fossil fuels such as coal and oil. Per capita electricity consumption measures the overall scale of electrification development and the degree of sharing across society, reflecting the intensity of energy activities and the level of modernization. Per capita residential electricity consumption reflects the depth of electricity penetration in residential life and the effectiveness of clean and low-carbon transformation. The average annual compound growth rate of electricity consumption quantifies the long-term growth momentum of electricity consumption, supporting demand forecasting, power planning, and the assessment of phased characteristics. Total electricity consumption reflects the overall demand for electricity from various sectors of the national economy and social life, serving as a core macroeconomic indicator for observing the coordinated evolution of electrification and economic operation. Urban and rural residential electricity consumption, separated from production activities, purely reflects the level of electrification in people's lives, measuring the depth and fairness of the benefits of electrification to people's livelihoods.

[0120] The power supply dimension measures the clean energy system's transformation and security capabilities. Installed power generation capacity is a fundamental indicator of the comprehensive supply capacity of power infrastructure and the level of energy self-sufficiency, reflecting the forward-looking layout of energy strategy. The proportion of non-fossil energy power generation directly depicts the clean energy transformation process on the power production side, reflecting the substantial progress in the energy structure shift from fossil-dominated to clean energy-dominated. The penetration rate of new energy power quantifies the actual contribution of new energy to end-user electricity consumption, marking a profound shift in the power system from guaranteed energy supply to green energy supply. The power supply reliability index is a key terminal indicator for evaluating the resilience and service quality of the power system, providing a safety guarantee for large-scale electricity substitution in industries, transportation, and construction.

[0121] The power energy efficiency dimension focuses on energy utilization efficiency and high-quality development level in the electrification process. The installed capacity of urban and rural residential users reflects residents' potential electricity consumption capacity and the level of modern infrastructure, providing necessary capacity guarantees for the introduction of low-carbon technologies such as electric vehicles and heat pumps. Electricity consumption per unit of GDP is a key indicator for measuring the economic benefits of power energy utilization, reflecting whether electrification development has shifted from scale expansion to a high-quality path of green growth. The electricity consumption elasticity coefficient, the ratio of electricity consumption growth rate to GDP growth rate, reveals the dynamic relationship between electricity consumption and economic growth, marking the transition of the electrification process from extensive scale-driven to a more intensive and higher-quality efficiency-driven stage.

[0122] 2. Data Preprocessing

[0123] The data in this study are sourced from the *China Energy Statistical Yearbook*, the *China Electricity Statistical Yearbook*, and the statistical yearbooks of Henan, Hubei, Hunan, and Jiangxi provinces. Supplementary data on power generation capacity, new energy operation, and power supply reliability are obtained from official statistical data released by the National Energy Administration, the State Grid Corporation of China, and provincial power companies. Regional GDP is uniformly converted to constant 2014 prices, and electricity consumption growth and elasticity coefficients are calculated using a five-year moving average to smooth fluctuations and ensure cross-year comparability and the reliability of medium- and long-term trends.

[0124] 3. Calculate the comprehensive index by combining entropy weight.

[0125] set up This represents the i-th sample (there are 4 provinces, 11 years of panel data, so i=1, 2, ..., m, m=44). This represents the original data for the j-th indicator of the i-th sample (there are 13 electrification indicators in total, so...). =1, 2, ..., n, n=13).

[0126] (1) Indicator standardization

[0127]

[0128] (2) Calculate the proportion of the j-th index of the i-th ordinal parameter. :

[0129]

[0130] (3) Calculate the entropy value of the j-th index:

[0131]

[0132]

[0133] (4) Based on the entropy value Calculate the redundancy of the j-th indicator. :

[0134]

[0135] (5) Calculate the weight of the j-th indicator based on the information entropy redundancy:

[0136]

[0137] (6) Calculate the individual scores for each indicator. :

[0138] The score of a single indicator for the i-th sample can be determined by its weight. Standardized value of index j The product yields the scores of indicator j in year i:

[0139] = *

[0140] (7) Calculate the overall electrification score :

[0141] The comprehensive electrification score for each province in different years represents the total contribution of all indicators within the calculation system to the system. This is generally achieved using integrated methods such as the geometric mean and linear weighting. Here, the linear weighting method is chosen for calculation:

[0142]

[0143] Evidence related to the technical effects obtained by the embodiments of the present invention.

[0144] 1. Comprehensive Energy Consumption Evaluation Index

[0145] (1) Data standardization and feature weighting of indicators

[0146] The table below shows the standardized scores of relevant energy consumption assessment indicators in the Central China region from 2014 to 2024. By mapping the original data to the [0,1] interval, the difference in dimensions is eliminated, laying the foundation for subsequent comprehensive evaluation.

[0147]

[0148] The characteristic weight is the basic premise for calculating the comprehensive energy consumption assessment index. It mainly explains the relative performance of each evaluation object under different energy consumption indicators and the discrete characteristics of the indicators, as shown in the table.

[0149]

[0150] (2) Entropy and weight of the indicator

[0151] The entropy values ​​and weights of the indicators primarily reflect the information content and core influence of each energy consumption evaluation indicator, achieving the goal of objective weighting and avoiding subjective bias. The magnitude of the entropy value directly corresponds to the information contribution capability of the indicator. The lower the entropy value, the greater the dispersion of the indicator, the richer the effective information it contains, and the stronger its ability to distinguish the comprehensive energy consumption levels of the four provinces, thus assigning a higher weight. Conversely, the higher the entropy value, the smaller the differences between the four provinces, the lower its contribution to the comprehensive assessment, and the lower its weight.

[0152]

[0153] (3) Evaluation matrix based on entropy weight

[0154] The entropy-weighted evaluation matrix reflects the comprehensive performance of each evaluation object after weighting, as shown in the table. This matrix organically combines the standardized data of each indicator with objective weights. Each element is the product of the standardized value of a certain energy consumption indicator in a certain province and the entropy weight of that indicator. This not only preserves the core information of the original energy consumption data, but also highlights the differences in importance of different indicators through weight assignment.

[0155]

[0156] (4) Distance from the positive and negative ideal solutions

[0157] The distance from the positive and negative ideal solutions represents the degree of deviation of each evaluated object from the optimal and worst energy consumption states, providing a quantitative basis for the gradient division of comprehensive energy consumption levels, as shown in the table. The positive ideal solution is the optimal weighted combination of indicators extracted based on the entropy-weighted evaluation matrix, representing the ideal state of comprehensive energy consumption levels in the four provinces of Central China; the negative ideal solution is the worst weighted combination of indicators, representing a poor state of regional energy consumption levels. In the table, the shorter the distance to the positive ideal solution and the longer the distance to the negative ideal solution for a province, the closer its comprehensive energy consumption level is to the ideal state, and the more significant the effectiveness of energy consumption control; conversely, it indicates that its comprehensive energy consumption level deviates far from the ideal state.

[0158]

[0159] (5) Calculate the relative proximity

[0160] Relative proximity is a standardized quantitative level and ranking result of the comprehensive energy consumption level of each evaluated object. It is the final core output of the entire comprehensive energy consumption assessment process, as shown in the table. Relative proximity is the comprehensive energy consumption assessment index, with a value ranging from 0 to 1. The closer the value is to 1, the closer the comprehensive energy consumption level of the province is to the ideal state; the closer the value is to 0, the closer its comprehensive energy consumption level is to a poor state. This table not only solves the problem of direct comparison and ranking of distance data, but also achieves standardized quantification of the comprehensive energy consumption levels of the four provinces, facilitating subsequent dynamic trend analysis and horizontal comparison.

[0161]

[0162] Based on the comprehensive energy consumption evaluation results, the overall energy consumption level in Central China showed a fluctuating upward trend from 2014 to 2024. The comprehensive evaluation index increased from 0.202 to 0.639, representing an overall improvement of approximately 44%. However, the cyclical fluctuations of the index also reflect the recurring nature of the energy transition process, and the sustainability of clean energy substitution and energy efficiency improvement still needs to be strengthened.

[0163] 2. Electrification Index

[0164] Taking Henan Province as an example, the standardization process is illustrated using the positive indicator, the proportion of electricity in final energy consumption. Standardization aims to eliminate the influence of different dimensions and orders of magnitude of indicators, making them comparable. For positive indicators, the standardized value is obtained by dividing the difference between the original value and the minimum value of the indicator across all samples by the range. For example, in 2014, the proportion of electricity in final energy consumption in Henan Province was 22.994%, while the minimum value of this indicator across all samples (years in the four provinces) was 15.171% (Hunan Province, 2014), and the maximum value was 35.017% (Jiangxi Province, 2024). According to the formula, the standardized result for the proportion of electricity in final energy consumption in Henan Province in 2014 is 0.410. For negative indicators, the standardization is obtained by dividing the difference between the maximum value and the original value by the range. The standardized data for other years and provinces are calculated using the same method. Through standardization, the values ​​of each indicator are mapped to... The interval effectively eliminated the dimensional differences, providing a reliable data foundation for subsequent weight calculations based on the entropy method and the measurement of the comprehensive electrification index.

[0165] (1) Characteristic proportion ( )

[0166] The characteristic weight is calculated as follows: the standardized value of a certain indicator in a given year is divided by the sum of the standardized values ​​of that indicator in all years and all provinces. Taking the indicator of the proportion of electricity in Henan Province's final energy consumption in 2014 as an example, its standardized value is 0.410. Summing up the standardized values ​​of this indicator in all samples (years in the four provinces), the total is 17.286. The proportion of Henan Province's standardized value of this indicator in 2014 to the total is the characteristic weight, calculated to be 0.024. This means that among all evaluated objects, Henan Province's relative contribution to this indicator in 2014 was approximately 2.4%. Similarly, the characteristic weights of other years and other indicators are calculated using this method. Through the calculation of characteristic weights, the absolute differences in the original data are transformed into structural proportional relationships, clearly presenting the relative positions of different years and different provinces on the same indicator, providing the necessary prerequisites for subsequent measurement of the degree of indicator variation based on information entropy.

[0167] (2) The entropy value of the indicator ( )

[0168] After obtaining the characteristic weights of each indicator, the information entropy of each indicator is further calculated. When calculating the entropy value, the coefficients are first determined. ,in This represents the total number of participants in the evaluation. This study covers data from four provinces—Henan, Hubei, Hunan, and Jiangxi—for a total of 11 years, from 2014 to 2024; therefore, the total sample size is [not specified]. For each indicator, its entropy value is calculated from the feature weight of all samples on that indicator, i.e., for... Multiply by the sum Taking the proportion of electricity in final energy consumption as an example, based on the characteristic proportions of the 44 samples calculated above, and substituting them into the above calculation process, the entropy value of this indicator is 0.942. Similarly, the entropy values ​​of the remaining 12 indicators are calculated sequentially, and the specific results are shown in the table. These entropy values ​​objectively reflect the degree of variation of each indicator in the evaluation of electrification level, laying a key foundation for subsequent weight determination.

[0169]

[0170] (3) Redundancy of indicators ( )

[0171] After obtaining the information entropy of each indicator, the redundancy (also known as the coefficient of variation or information utility value) of the indicator can be further calculated. Redundancy is inversely related to entropy; the smaller the entropy value, the greater the redundancy, indicating a more significant degree of variation in the indicator data. The redundancy calculation results for each indicator are shown in the table.

[0172]

[0173] (4) Weights of indicators ( )

[0174] After obtaining the redundancy of each indicator, the weight of each indicator can be calculated. The weight reflects the relative importance of each indicator in the comprehensive evaluation of electrification level, and its value is determined by the proportion of the redundancy of that indicator to the total redundancy of all indicators. The greater the redundancy of an indicator, the more significant its contribution to the evaluation system, and the higher its corresponding weight. The redundancy calculation results of each indicator are shown in the table.

[0175]

[0176] (5) Individual scores of indicators ( )

[0177] Based on the weight calculation of each indicator, the individual score of each indicator is further calculated. The individual score of an indicator is obtained by multiplying its standardized value by its weight, reflecting the magnitude of the indicator's contribution to the comprehensive evaluation of electrification level. The higher the individual score, the better the level of electrification development represented by the indicator, and the greater its positive contribution to the comprehensive score. Taking the indicator of the proportion of electricity in final energy consumption in Henan Province in 2014 as an example, its standardized value is 0.410, with a corresponding weight of 0.091. Multiplying the standardized value by the weight yields an individual score of 0.037 for this indicator. This result indicates that the indicator of the proportion of electricity in final energy consumption contributed 0.037 points in the 2014 evaluation of Henan Province's electrification level. The individual scores of all indicators for each year and province are calculated using this method, that is, multiplying the standardized value of each indicator by its corresponding weight. The individual scores of the indicators are not only the basis for the calculation of dimensional scores and comprehensive indexes, but also can intuitively present the differences in the performance of each indicator in specific years and provinces, providing a basis for in-depth analysis of the structural characteristics of electrification level.

[0178] (6) Overall score of electrification

[0179] Based on the calculation of individual scores for each indicator, the overall electrification score for each province for each year is further aggregated. The overall electrification score is obtained by summing the individual scores of all 13 indicators, reflecting the overall electrification level of each province in a given year. The higher the overall score, the better the province's electrification development level in that year. Taking Henan Province in 2014 as an example, the overall electrification score after summing the individual scores of all 13 indicators for that year was 0.149. Similarly, the overall electrification scores for other provinces for each year are calculated using the same method. The calculation results of the overall electrification scores for each province from 2014 to 2024 are shown in the table below.

[0180]

[0181] It should be noted that embodiments of the present invention can be implemented in hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by a suitable instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the above-described devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuitry such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of the above-described hardware circuitry and software, such as firmware.

[0182] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A comprehensive energy consumption evaluation index construction system, characterized in that, The system includes a multi-dimensional collaborative measurement framework generation unit and a comprehensive index calculation unit; The multidimensional collaborative measurement framework generation unit is used to construct a measurement system that includes the total scale dimension, economic efficiency dimension, structural transformation dimension, and dynamic trend dimension. Among them, the total scale dimension is configured with total energy consumption index and total carbon emission index, the economic efficiency dimension is configured with energy consumption per unit of GDP index and carbon emission per unit of GDP index, the structural transformation dimension is configured with clean energy ratio index, and the dynamic trend dimension is configured with energy consumption change rate per unit of GDP index and energy consumption elasticity coefficient index. The comprehensive index calculation unit is used to combine the standardized data of the above indicators with the objective weights determined based on the entropy weight method to calculate the comprehensive energy consumption evaluation index. Extended interaction effect dimensionality reduction analysis: In response to the nonlinear interference of different regional industrial structures on the evaluation index, a coupling coordination degree model is introduced to correct the linear superposition of indicators, which significantly improves the authenticity and scientific differentiation ability of the comprehensive evaluation.

2. The comprehensive energy consumption evaluation index construction system according to claim 1, characterized in that, The system also includes an index calculation unit for performing the following calculations: The total energy consumption index is calculated by multiplying the physical quantity of the final consumption of the i-th type of energy by the conversion factor of the i-th type of energy, and then summing the results over all energy types. The total carbon emission index is calculated by multiplying the physical amount of final consumption of the i-th energy source by the carbon dioxide emission factor of the i-th energy source, and then summing the results over all energy types. The energy consumption per unit of GDP is calculated as follows: multiply the physical quantity of various types of final energy in period t by the corresponding standard coal equivalent coefficient and sum them up, then divide by the regional GDP at constant prices in the same period. The carbon emission index per unit of GDP is calculated by dividing the total carbon emissions in year t by the regional GDP at constant prices in the same period. The clean energy share indicator is calculated by dividing clean energy consumption by total energy consumption in the same period. The calculation method for the rate of change in energy consumption per unit of GDP is as follows: divide the difference between the energy consumption per unit of GDP in period t and the previous period by the energy consumption per unit of GDP in the previous period. The energy consumption elasticity coefficient is calculated by dividing the rate of change in energy consumption by the rate of change in GDP at constant prices.

3. The comprehensive energy consumption evaluation index construction system according to claim 1, characterized in that, The system also includes a data preprocessing unit for performing the following steps: Energy data is obtained by acquiring the end-use physical consumption of multiple energy types based on the energy balance sheet; Extract the annual GDP and industrial added value of each region as economic data; The physical quantities of each energy type are uniformly converted into standard coal equivalents.

4. The comprehensive energy consumption evaluation index construction system according to claim 1, characterized in that, The composite index calculation unit is also used to perform the following steps: The range of each indicator was standardized. Calculate the characteristic weight of each evaluation object on each indicator; The entropy value of each indicator is calculated based on the feature weight; Calculate the redundancy of each indicator based on the entropy value; The weights of each indicator are calculated based on redundancy. Construct a weighted matrix; Determine the optimal and worst solutions based on the weighted matrix; Calculate the Euclidean distance between each evaluation object and the optimal and worst solutions; The relative proximity is calculated based on the Euclidean distance, and this relative proximity is used as the comprehensive energy consumption evaluation index.

5. A method for constructing a comprehensive electrification index, characterized in that, This method is based on the comprehensive energy consumption evaluation index construction system described in any one of claims 1 to 4. For multiple electrification indicators in the electrification evaluation system, the method uses the entropy weight method to determine the objective weight of each indicator, and calculates the relative proximity of each evaluation object based on the TOPSIS method. The relative proximity is used as the comprehensive electrification score to evaluate the level of electrification development.

6. The method for constructing the comprehensive electrification index according to claim 5, characterized in that, The method also includes: standardizing multiple electrification indicators; calculating the characteristic weight of each evaluation object on each electrification indicator, wherein the characteristic weight is the standardized value of a certain indicator divided by the sum of all standardized values ​​of that indicator.

7. The method for constructing the comprehensive electrification index according to claim 6, characterized in that, The method also includes: calculating the entropy value of each electrification index based on the characteristic weight of each index, which reflects the degree of variation of each index.

8. The method for constructing the comprehensive electrification index according to claim 7, characterized in that, The method also includes: calculating the redundancy of each electrification index based on the entropy value of each index, where the redundancy is the information utility value.

9. The method for constructing the comprehensive electrification index according to claim 8, characterized in that, The method also includes: calculating the weight of each electrification index based on the redundancy of each index, the weight being determined by the proportion of the redundancy of the index to the total redundancy of all indices.

10. The method for constructing the comprehensive electrification index according to claim 9, characterized in that, The method further includes: multiplying the standardized value of each electrification indicator by its corresponding weight to obtain the individual score of the indicator; and summing the individual scores of all electrification indicators of the evaluation object to obtain the comprehensive electrification score.

Images