WO2023231198A1 - Comprehensive evaluation method for carbon neutrality based on sparse logarithmic principal component analysis - Google Patents
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- the invention relates to the technical field of carbon neutrality evaluation, and in particular to a carbon neutrality comprehensive evaluation method based on sparse logarithmic principal component analysis.
- the construction of the existing carbon neutrality index covers various indicators of economic and social transformation such as economic development, industrial characteristics, energy structure, technological innovation, finance and taxation, environmental quality, ecological governance, policy and public opinion, etc.
- the indicator system is large and covers a wide range of information, which can fully reflect the level of carbon-neutral development.
- the huge indicator system also has the problem of indicator redundancy.
- the existing carbon neutral index construction method incorporates all indicators in the indicator pool into index modeling and cannot eliminate redundant indicators.
- the principal component comprehensive evaluation method is the main objective comprehensive evaluation method. Most of the current mainstream principal component comprehensive evaluation methods select multiple principal components based on the variance contribution rate of the principal components, and use the loadings of principal component analysis for linear weighting. When this processing method uses load coefficients in different directions, the evaluation results vary greatly, and the problem of unbalanced development in the index system cannot be identified.
- the purpose of the present invention is to provide a carbon neutrality comprehensive evaluation method based on sparse logarithmic principal component analysis, which can solve the problems of redundant indicators and large differences in evaluation results in the evaluation index system of the prior art.
- a comprehensive evaluation method for carbon neutrality based on sparse logarithmic principal component analysis including the following steps:
- the multi-dimensional carbon neutrality index system includes first-level indicators and second-level indicators. level indicators;
- first-level indicators are used to represent analysis dimensions; the second-level indicators are used to represent analysis indicators under each analysis dimension.
- data preprocessing for the secondary indicators includes: indicator forwarding, indicator normalization and indicator logarithmization.
- the indicator is positive, specifically: based on the positive and negative impact of each secondary indicator on the carbon neutrality goal, it is judged whether the secondary indicator is higher than the preset threshold, and if it is higher, the secondary indicator is set.
- the indicator is a positive indicator; otherwise, the secondary indicator is set as a negative indicator.
- index normalization process is as shown in the following formula:
- the process of using sparse principal component analysis to weight the secondary indicators of each dimension includes:
- Each indicator of the kth indicator dimension has no normalized weight coefficient ⁇ .
- the solution process of the weight coefficient is as follows:
- ⁇ and ⁇ 1 are adjustment parameters, and the choice of ⁇ 1 plays a decisive role in the sparsity of the weight; is the sparse first principal component loading coefficient; ⁇ represents the unit constant vector. Represents the unitized constant vector corresponding to the kth indicator. After normalization, the weight coefficient of each indicator in the kth indicator dimension is:
- the entropy weight method is used to weight the first-level indicators, specifically:
- the weight of the kth primary indicator is calculated using the entropy weight of the secondary indicator.
- n the region number
- Carbon Index i represents the final carbon neutrality index of the i-th region
- v (k) represents the entropy of the k-th first-level indicator.
- the weight obtained by the power method Represents the weight obtained by sparse principal component weighting of the j-th second-level indicator in the k-th first-level indicator.
- the present invention introduces the logarithmic principal component comprehensive evaluation method, and takes the normalized load corresponding to the first principal component as the weight coefficient for geometric weighting. Taking the first principal component ensures the consistency of the index results. Geometric weighting can effectively identify the problem of unbalanced development in the indicator system and penalize areas with unbalanced development in terms of index scores. Based on the existing carbon neutrality index index system, the present invention introduces the sparse principal component analysis method into the index construction method, which can effectively screen out important indicators of the indicator pool and streamline the indicator pool.
- Figure 1 is a step diagram of the carbon neutrality comprehensive evaluation method based on sparse logarithmic principal component analysis of the present invention.
- the carbon neutrality comprehensive evaluation method based on sparse logarithmic principal component analysis of the present invention includes the following steps:
- Step S1 Construct a multi-dimensional carbon neutrality index system.
- the multi-dimensional carbon neutrality indicator system includes two parts: primary indicators and secondary indicators.
- the first-level indicators are used to represent the analysis dimensions, including at least: economic development, industrial characteristics, green industry, energy structure, infrastructure level, technological innovation, finance and taxation, ecological construction, environmental quality and green life, but are not limited to these.
- Secondary indicators are used to represent specific analysis indicators under each analysis dimension, including at least:
- Green industry electricity consumption per unit industrial added value, increase or decrease in energy consumption per 10,000 yuan of regional GDP, water consumption per unit industrial added value, gas emissions per unit industrial added value, wastewater discharge per unit industrial added value, investment in industrial wastewater treatment, Investment in process waste gas pollution control;
- Infrastructure level road area in built-up areas, highway passenger volume, railway operating mileage growth rate, proportion of bus-only road length, hydropower installed capacity growth rate, and number of mobile phone base stations;
- Financial finance and taxation fiscal revenue, fiscal expenditure, energy conservation and environmental protection expenditure, tax revenue as a share of GDP, local fiscal expenditure as a share of GDP, the number of financial practitioners, and the added value of the financial industry;
- Green life the harmless treatment rate of domestic waste, the number of buses per 10,000 people, the proportion of new energy vehicle consumption, and the proportion of urban green buildings in new buildings.
- Step S2 Perform data preprocessing on the indicators.
- data preprocessing of indicators includes: indicator forwarding, indicator normalization and indicator logarithmization.
- the positive indicator is divided into positive indicators and negative indicators based on the positive and negative impact of each secondary indicator on the carbon neutrality goal. If the indicator value is higher than the set threshold, the indicator is set as a positive indicator; otherwise, it is set as a negative indicator.
- index value of the j-th second-level indicator of the k-th first-level indicator in the i-th region is the normalized index.
- Step S3 Use the sparse principal component analysis method to weight the secondary indicators of each dimension.
- the method of using sparse principal component analysis is to apply a norm penalty to the loading coefficient based on traditional principal component analysis, so that the weight of some unimportant indicators in the principal component composition is 0.
- the estimation process of its load sparseness is as follows:
- Each indicator of the kth indicator dimension has unnormalized weight coefficient ⁇ .
- the solution process of the weight coefficient is as follows:
- ⁇ and ⁇ 1 are adjustment parameters, and the choice of ⁇ 1 plays a decisive role in the sparsity of the weight.
- ⁇ represents the unit constant vector. Represents the unitized constant vector corresponding to the kth indicator. After normalization, the weight coefficient of each indicator in the kth indicator dimension is:
- the method of sampling sparse principal component analysis can effectively screen out important indicators in the indicator pool, streamline the indicator pool, and effectively avoid the problem of indicator redundancy in a huge indicator system.
- Step S4 Use the entropy weight method to weight the first-level indicators.
- Step S5 Calculate the carbon neutrality index and conduct a comprehensive evaluation of regional carbon neutrality.
- Carbon Index i represents the final carbon neutrality index of the i-th region
- v (k) represents the entropy of the k-th first-level indicator.
- the weight obtained by the power method Represents the weight obtained by sparse principal component weighting of the j-th second-level indicator in the k-th first-level indicator.
- a comprehensive evaluation of the degree of carbon neutrality in different regions can be made based on the calculated carbon neutrality index. The higher the index value, the better the completion of carbon neutrality.
- the present invention improves the index weighting method and introduces the method of sparse principal component analysis, which can effectively screen out important indicators of the indicator pool, streamline the indicator pool, and effectively avoid The huge indicator system has the problem of indicator redundancy.
- the present invention performs logarithmic processing on the normalized index during the index preprocessing process; and uses geometric weighting to replace traditional linear algebra weighting during the index synthesis process.
- This processing method enables the carbon neutrality index obtained by the present invention to effectively measure the problem of unbalanced development among indicators.
- This invention takes the sparse first principal component as the secondary index weight, uses the entropy weight method to solve the primary index weight, and makes the index result more robust through a two-stage weight fusion method.
- connection In the present invention, unless otherwise clearly stated and limited, the terms “installation”, “connection”, “connection”, “fixing” and other terms should be understood in a broad sense. For example, it can be a fixed connection or a detachable connection. , or integrated; it can be mechanically connected, electrically connected or communicable with each other; it can be directly connected or indirectly connected through an intermediate medium; it can be the internal connection of two elements or the interaction between two elements, Unless otherwise expressly limited. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.
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Abstract
A comprehensive evaluation method for carbon neutrality based on sparse logarithmic principal component analysis, comprising the following steps: constructing a multi-dimensional carbon neutrality indicator system; performing data preprocessing on indicators; assigning a weight to second-level indicators of dimensions by using a sparse principal component analysis method; assigning a weight to first-level indicators by using an entropy weight method; and calculating a carbon neutrality index to perform comprehensive evaluation of regional carbon neutrality. According to the present invention, on the basis of an existing carbon neutrality index indicator system, the method for assigning a weight to the indicators is improved, and the sparse principal component analysis method is introduced, such that important indicators of an indictor pool can be effectively screened out, the indictor pool can be simplified, and the problem of indictor redundancy existing in a huge indictor system can be effectively avoided. According to the present invention, a first sparse main component is taken as the weight of the second-level indicators, the weight of the first-level indicators is solved by using the entropy weight method, and the index result is more robust by means of a method for fusing the weights at two stages.
Description
本发明涉及碳中和评价技术领域,特别涉及一种基于稀疏对数主成分分析的碳中和综合评价方法。The invention relates to the technical field of carbon neutrality evaluation, and in particular to a carbon neutrality comprehensive evaluation method based on sparse logarithmic principal component analysis.
气候变化是人类面临的全球性问题,随着各国二氧化碳排放,温室气体猛增,对生命系统形成威胁。在这一背景下,世界各国以全球协约的方式减排温室气体,2020年9月,中国在联合国大会上向世界宣布了2030年前实现碳达峰、2060年前实现碳中和的目标。构建的一套客观、系统、全面、综合、动态的碳中和评价体系以评价地区碳中和发展水平引发国内外学者的广泛研究。Climate change is a global problem facing mankind. As countries emit carbon dioxide, greenhouse gases increase sharply, posing a threat to life systems. Against this background, countries around the world are reducing greenhouse gas emissions in the form of a global agreement. In September 2020, China announced to the world at the United Nations General Assembly the goal of peaking carbon emissions by 2030 and achieving carbon neutrality by 2060. The construction of an objective, systematic, comprehensive, comprehensive and dynamic carbon neutrality evaluation system to evaluate the regional carbon neutrality development level has triggered extensive research by domestic and foreign scholars.
现有碳中和指数的构建涵盖经济发展、产业特征、能源结构、技术创新、金融财税、环境质量、生态治理、政策舆情等经济社会转型的各方面指标。指标体系庞大,信息覆盖面广,能够充分反映碳中和发展水平。但是庞大的指标体系也存在着指标冗余的问题。现有碳中和指数构建的方法将指标池的所有指标都纳入指数建模,无法剔除冗余指标。The construction of the existing carbon neutrality index covers various indicators of economic and social transformation such as economic development, industrial characteristics, energy structure, technological innovation, finance and taxation, environmental quality, ecological governance, policy and public opinion, etc. The indicator system is large and covers a wide range of information, which can fully reflect the level of carbon-neutral development. However, the huge indicator system also has the problem of indicator redundancy. The existing carbon neutral index construction method incorporates all indicators in the indicator pool into index modeling and cannot eliminate redundant indicators.
现有综合评价方法主要分为主观综合评价法和客观综合评价法。主成分综合评价法是主要的客观综合评价方法。当前主流的主成分综合评价法大多根据主成分的方差贡献率选取多个主成分,并使用主成分分析的载荷进行线性加权。这种处理方式在采用不同方向载荷系数时,评价结果差异较大,并且无法识别指标体系中发展不平衡的问题。Existing comprehensive evaluation methods are mainly divided into subjective comprehensive evaluation methods and objective comprehensive evaluation methods. The principal component comprehensive evaluation method is the main objective comprehensive evaluation method. Most of the current mainstream principal component comprehensive evaluation methods select multiple principal components based on the variance contribution rate of the principal components, and use the loadings of principal component analysis for linear weighting. When this processing method uses load coefficients in different directions, the evaluation results vary greatly, and the problem of unbalanced development in the index system cannot be identified.
发明内容Contents of the invention
本发明的目的是提供一种基于稀疏对数主成分分析的碳中和综合评价方法,可以解决现有技术的评价指标体系存在冗余指标,以及评价结果差异较大的问题。The purpose of the present invention is to provide a carbon neutrality comprehensive evaluation method based on sparse logarithmic principal component analysis, which can solve the problems of redundant indicators and large differences in evaluation results in the evaluation index system of the prior art.
本发明的目的是通过以下技术方案来实现的:The purpose of the present invention is achieved through the following technical solutions:
一种基于稀疏对数主成分分析的碳中和综合评价方法,包括以下步骤:A comprehensive evaluation method for carbon neutrality based on sparse logarithmic principal component analysis, including the following steps:
根据碳排放情况采集行业信息作为评价指标,按类别对评价指标进行人工标引,利用人工标引的评价指标构建多维碳中和指标体系;所述多维碳中和指标体系包括一级指标和二级指标;Collect industry information based on carbon emissions as evaluation indicators, manually index the evaluation indicators by category, and use the manually indexed evaluation indicators to build a multi-dimensional carbon neutrality index system; the multi-dimensional carbon neutrality index system includes first-level indicators and second-level indicators. level indicators;
对所述二级指标进行数据预处理;Perform data preprocessing on the secondary indicators;
使用稀疏主成分分析的方法对所述二级指标进行赋权;Use sparse principal component analysis method to weight the secondary indicators;
使用熵权法对所述一级指标进行赋权;Use the entropy weight method to weight the first-level indicators;
计算碳中和指数,根据将所述碳中和指数进行地区碳中和的综合评价。Calculate the carbon neutrality index, and conduct a comprehensive evaluation of regional carbon neutrality based on the carbon neutrality index.
进一步的,所述一级指标用于表示分析维度;所述二级指标用于表示每一个分析维度下的分析指标。Furthermore, the first-level indicators are used to represent analysis dimensions; the second-level indicators are used to represent analysis indicators under each analysis dimension.
进一步的,对所述二级指标进行数据预处理包括:指标正向化、指标归一化和指标对数化。Further, data preprocessing for the secondary indicators includes: indicator forwarding, indicator normalization and indicator logarithmization.
进一步的,所述指标正向化,具体为:根据各二级指标对碳中和目标的正负面影响,判断所述二级指标是否高于预设阈值,若高于,则设置该二级指标为正向指标;否则,则设置该二级指标为负向指标。Further, the indicator is positive, specifically: based on the positive and negative impact of each secondary indicator on the carbon neutrality goal, it is judged whether the secondary indicator is higher than the preset threshold, and if it is higher, the secondary indicator is set. The indicator is a positive indicator; otherwise, the secondary indicator is set as a negative indicator.
进一步的,所述指标归一化过程如下公式所示:Further, the index normalization process is as shown in the following formula:
其中,
为第i个地区第k个维度一级指标所属的第j个二级指标值,
为归一化后的指标。
in, is the j-th second-level indicator value belonging to the first-level indicator of the k-th dimension in the i-th region, is the normalized index.
进一步的,所述指标对数化过程如下公式所示:Further, the logarithmic process of the indicator is as shown in the following formula:
其中,
为第i个地区第k个维度一级指标所属的第j个二级指标经预处理后的结果,
为归一化后的指标。
in, is the preprocessed result of the j-th second-level indicator belonging to the k-th dimension first-level indicator in the i-th region, is the normalized index.
进一步的,所述使用稀疏主成分分析的方法对各维度二级指标进行赋权的过程包括:Further, the process of using sparse principal component analysis to weight the secondary indicators of each dimension includes:
记
为第i个地区的第k个一级指标中的第j个经预处理后的指标值,第k个指标维度所构成的指标矩阵为
第k个指标维度的各指标未归一化权重系数β,权重系数的求解过程如下:
remember is the preprocessed index value of the j-th among the k-th first-level indicators in the i-th region, and the indicator matrix composed of the k-th indicator dimension is: Each indicator of the kth indicator dimension has no normalized weight coefficient β. The solution process of the weight coefficient is as follows:
其中,
λ和λ
1是调节参数,λ
1的选择对权重稀疏性取决定性作用;
是稀疏的第一主成分载荷系数;α表示单位常向量。
表示第k个指标所对应的单位化单位常向量。经过归一化可得,第k个指标维度的各指标的权重系数为:
in, λ and λ 1 are adjustment parameters, and the choice of λ 1 plays a decisive role in the sparsity of the weight; is the sparse first principal component loading coefficient; α represents the unit constant vector. Represents the unitized constant vector corresponding to the kth indicator. After normalization, the weight coefficient of each indicator in the kth indicator dimension is:
进一步的,所述使用熵权法对一级指标进行赋权,具体为:Further, the entropy weight method is used to weight the first-level indicators, specifically:
计算第k个一级指标中第j个二级指标的信息熵;Calculate the information entropy of the j-th secondary indicator among the k-th primary indicator;
利用所述二级指标的信息熵计算第k个一级指标中第j个二级指标的熵权;Calculate the entropy weight of the j-th secondary indicator in the k-th primary indicator using the information entropy of the secondary indicator;
利用所述二级指标的熵权计算第k个一级指标的权重。The weight of the kth primary indicator is calculated using the entropy weight of the secondary indicator.
进一步的,所述计算第k个一级指标的权重的公式为:Further, the formula for calculating the weight of the kth first-level indicator is:
其中
为第k个一级指标中第j个二级指标的熵权,
的计算公式为:
in is the entropy weight of the j-th secondary indicator among the k-th primary indicator, The calculation formula is:
其中
为第k个一级指标中第j个二级指标的信息熵,
的计算公式为:
in is the information entropy of the j-th secondary indicator among the k-th primary indicator, The calculation formula is:
其中
为第i个地区的第k个一级指标中的第j个二级指标经预处理后的指标值,n表示地区编号。
in is the preprocessed index value of the j-th second-level indicator among the k-th first-level indicators in the i-th region, and n represents the region number.
进一步的,所述计算碳中和指数,具体为:Further, the calculation of the carbon neutrality index is specifically as follows:
其中,Carbon Index
i表示第i个地区最终的碳中和指数,
表示第i个地区第k个一级指标中第j个二级指标经过正向化、归一化,但未经过对数化的指标值,v
(k)代表第k个一级指标经熵权法得到的权重,
表示第k个一级指标中的第j个二级指标经稀疏主成分赋权得到的权重。
Among them, Carbon Index i represents the final carbon neutrality index of the i-th region, Represents the index value of the j-th second-level indicator among the k-th first-level indicator in the i-th region that has been forwarded and normalized but not logarithmized. v (k) represents the entropy of the k-th first-level indicator. The weight obtained by the power method, Represents the weight obtained by sparse principal component weighting of the j-th second-level indicator in the k-th first-level indicator.
本发明引入对数化主成分综合评价方法,并取第一主成分对应的归一化载荷作为权重系数进行几何加权。取第一主成分保证了指数结果的一致性,几何加权可以有效识别指标体系中发展不平衡的问题,对发展不平衡的地区进行指数得分上的惩罚。本发明基于现有碳中和指数的指标体系基础上,在指数构建方法中引入稀疏主成分分析方法,能够有效地筛选出指标池的重要指标,精简指标池。The present invention introduces the logarithmic principal component comprehensive evaluation method, and takes the normalized load corresponding to the first principal component as the weight coefficient for geometric weighting. Taking the first principal component ensures the consistency of the index results. Geometric weighting can effectively identify the problem of unbalanced development in the indicator system and penalize areas with unbalanced development in terms of index scores. Based on the existing carbon neutrality index index system, the present invention introduces the sparse principal component analysis method into the index construction method, which can effectively screen out important indicators of the indicator pool and streamline the indicator pool.
图1为本发明的基于稀疏对数主成分分析的碳中和综合评价方法的步骤图。Figure 1 is a step diagram of the carbon neutrality comprehensive evaluation method based on sparse logarithmic principal component analysis of the present invention.
下面结合附图对本公开实施例进行详细描述。The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.
以下通过特定的具体实例说明本公开的实施方式,本领域技术人员可由本 说明书所揭露的内容轻易地了解本公开的其他优点与功效。显然,所描述的实施例仅仅是本公开一部分实施例,而不是全部的实施例。本公开还可以通过另外不同的具体实施方式加以实施或应用,本说明书中的各项细节也可以基于不同观点与应用,在没有背离本公开的精神下进行各种修饰或改变。需说明的是,在不冲突的情况下,以下实施例及实施例中的特征可以相互组合。基于本公开中的实施例,本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例,都属于本公开保护的范围。The following describes the embodiments of the present disclosure through specific examples. Those skilled in the art can easily understand other advantages and effects of the present disclosure from the content disclosed in this specification. Obviously, the described embodiments are only some, but not all, of the embodiments of the present disclosure. The present disclosure can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the disclosure. It should be noted that, as long as there is no conflict, the following embodiments and the features in the embodiments can be combined with each other. Based on the embodiments in this disclosure, all other embodiments obtained by those of ordinary skill in the art without making creative efforts fall within the scope of protection of this disclosure.
本发明的基于稀疏对数主成分分析的碳中和综合评价方法,包括以下步骤:The carbon neutrality comprehensive evaluation method based on sparse logarithmic principal component analysis of the present invention includes the following steps:
步骤S1、构建多维碳中和指标体系。Step S1: Construct a multi-dimensional carbon neutrality index system.
多维碳中和指标体系包括一级指标和二级指标两部分。The multi-dimensional carbon neutrality indicator system includes two parts: primary indicators and secondary indicators.
其中,一级指标用于表示分析维度,至少包括:经济发展、产业特征、绿色工业、能源结构、基建水平、科技创新、金融财税、生态建设、环境质量和绿色生活,但不限于此。Among them, the first-level indicators are used to represent the analysis dimensions, including at least: economic development, industrial characteristics, green industry, energy structure, infrastructure level, technological innovation, finance and taxation, ecological construction, environmental quality and green life, but are not limited to these.
二级指标用于表示每一个分析维度下的具体分析指标,至少包括:Secondary indicators are used to represent specific analysis indicators under each analysis dimension, including at least:
(1)经济发展:人均地区生产总值、进出口总额、固定资产投资增速、居民人均可支配收入、城镇登记失业率、城镇化率、人口老龄化率;(1) Economic development: per capita regional GDP, total import and export volume, fixed asset investment growth rate, per capita disposable income of residents, urban registered unemployment rate, urbanization rate, and population aging rate;
(2)产业特征:产业高级化、产业结构偏离度、IPV4地址数、软件业务收入、电子商务销售额、高技术产业新产品销售收入、能源工业投资增速、石油加工投资增速、煤炭采选业投资增速;(2) Industry characteristics: industrial upgrading, industrial structure deviation, number of IPV4 addresses, software business revenue, e-commerce sales, sales revenue of new high-tech industry products, energy industry investment growth, petroleum processing investment growth, coal mining Industry investment growth rate;
(3)绿色工业:单位工业增加值电耗、万元地区生产总值能耗增减、单位工业增加值用水量、单位工业增加值燃气排放、单位工业增加值废水排放、工业废水治理投资、工艺废气污染治理投资;(3) Green industry: electricity consumption per unit industrial added value, increase or decrease in energy consumption per 10,000 yuan of regional GDP, water consumption per unit industrial added value, gas emissions per unit industrial added value, wastewater discharge per unit industrial added value, investment in industrial wastewater treatment, Investment in process waste gas pollution control;
(4)能源结构:煤炭消费占比、原油消费占比、天然气消费占比、电力消费占比、化石能源发电量占比、发电量、可再生能源发电量占比;(4) Energy structure: proportion of coal consumption, proportion of crude oil consumption, proportion of natural gas consumption, proportion of electricity consumption, proportion of fossil energy power generation, proportion of power generation, and proportion of renewable energy power generation;
(5)基建水平:建成区道路面积、公路客运量、铁路运营里程增速、公交专用道路长度占比、水电装机容量增速、移动电话基站数;(5) Infrastructure level: road area in built-up areas, highway passenger volume, railway operating mileage growth rate, proportion of bus-only road length, hydropower installed capacity growth rate, and number of mobile phone base stations;
(6)科技创新:科技版企业数量、科技论文发表数量、绿色专利占比、技术市场成交额、有效发明专利数;(6) Scientific and technological innovation: number of scientific and technological enterprises, number of scientific papers published, proportion of green patents, technology market turnover, and number of valid invention patents;
(7)金融财税:财政收入、财政支出、节能环保支出、税收收入占GDP比重、地方财政支出占GDP比重、金融从业人数占比、金融业增加值占比;(7) Financial finance and taxation: fiscal revenue, fiscal expenditure, energy conservation and environmental protection expenditure, tax revenue as a share of GDP, local fiscal expenditure as a share of GDP, the number of financial practitioners, and the added value of the financial industry;
(8)生态建设:公路绿地面积占比、建成区绿化覆盖率、沙土化面积占比、新增水土流失治理面积、国家级自然保护区面积占比、湿地面积、森林面积、森林覆盖率;(8) Ecological construction: proportion of highway green space, green coverage rate of built-up areas, proportion of sandy soil area, newly added water and soil erosion control area, proportion of national nature reserve area, wetland area, forest area, forest coverage rate;
(9)环境质量:化肥使用量、农药使用量、碳排放量、二氧化硫排放量、氮氧化物排放量;(9) Environmental quality: fertilizer usage, pesticide usage, carbon emissions, sulfur dioxide emissions, nitrogen oxide emissions;
(10)绿色生活:生活垃圾无害化处理率、每万人拥有公共汽车量、新能源汽车消费占比、城市绿色建筑占新建建筑比。(10) Green life: the harmless treatment rate of domestic waste, the number of buses per 10,000 people, the proportion of new energy vehicle consumption, and the proportion of urban green buildings in new buildings.
步骤S2、对指标进行数据预处理。Step S2: Perform data preprocessing on the indicators.
进一步的,在本申请的优选实施方式中,对指标进行数据预处理包括:指标正向化、指标归一化和指标对数化。Further, in the preferred embodiment of the present application, data preprocessing of indicators includes: indicator forwarding, indicator normalization and indicator logarithmization.
指标正向化即根据各二级指标对碳中和目标的正负面影响划分为正向指标和负向指标。指标值高于设定的阈值,则设置该指标为正向指标;反之设置为负向指标。The positive indicator is divided into positive indicators and negative indicators based on the positive and negative impact of each secondary indicator on the carbon neutrality goal. If the indicator value is higher than the set threshold, the indicator is set as a positive indicator; otherwise, it is set as a negative indicator.
指标归一化过程如下公式所示:The indicator normalization process is shown in the following formula:
其中,
为第i个地区第k个维度一级指标所属的第j个二级指标值,
为 归一化后的指标。
in, is the j-th second-level indicator value belonging to the first-level indicator of the k-th dimension in the i-th region, is the normalized index.
指标对数化过程如下公式所示:The index logarithmization process is shown in the following formula:
其中,
为第i个地区第k个一级指标的第j个二级指标经预处理后的指标值,
为归一化后的指标。
in, is the preprocessed index value of the j-th second-level indicator of the k-th first-level indicator in the i-th region, is the normalized index.
步骤S3、使用稀疏主成分分析的方法对各维度二级指标进行赋权。Step S3: Use the sparse principal component analysis method to weight the secondary indicators of each dimension.
使用稀疏主成分分析的方法是在传统主成分分析基础上,对载荷系数施加一范数惩罚,使一些不重要的指标在主成分构成中的权重为0。其载荷稀疏的估计过程如下:The method of using sparse principal component analysis is to apply a norm penalty to the loading coefficient based on traditional principal component analysis, so that the weight of some unimportant indicators in the principal component composition is 0. The estimation process of its load sparseness is as follows:
记
为第i个地区的第k个一级指标中的第j个经预处理后的指标值,第k个指标维度所构成的指标矩阵为
第k个指标维度的各指标未归一化权重系数β。权重系数的求解过程如下:
remember is the preprocessed index value of the j-th among the k-th first-level indicators in the i-th region, and the indicator matrix composed of the k-th indicator dimension is: Each indicator of the kth indicator dimension has unnormalized weight coefficient β. The solution process of the weight coefficient is as follows:
其中,
λ和λ
1是调节参数,λ
1的选择对权重稀疏性取决定性作用。
是稀疏的第一主成分载荷系数。α表示单位常向量。
表示第k个指标所对应的单位化单位常向量。经过归一化可得,第k个指标维度的各指标的权重系数为:
in, λ and λ 1 are adjustment parameters, and the choice of λ 1 plays a decisive role in the sparsity of the weight. is the sparse first principal component loading coefficient. α represents the unit constant vector. Represents the unitized constant vector corresponding to the kth indicator. After normalization, the weight coefficient of each indicator in the kth indicator dimension is:
采样稀疏主成分分析的方法,能够有效地筛选出指标池的重要指标,精简指标池,有效避免了庞大的指标体系存在的指标冗余的问题。The method of sampling sparse principal component analysis can effectively screen out important indicators in the indicator pool, streamline the indicator pool, and effectively avoid the problem of indicator redundancy in a huge indicator system.
步骤S4、使用熵权法对一级指标进行赋权。Step S4: Use the entropy weight method to weight the first-level indicators.
使用熵权法对一级指标进行赋权的具体过程如下:The specific process of using the entropy weight method to weight first-level indicators is as follows:
(1)计算第k个一级指标中第j个二级指标的信息熵:(1) Calculate the information entropy of the j-th secondary indicator in the k-th primary indicator:
其中,
表示第i个地区的第k个一级指标中的第j个二级指标的归一化权重,
为第i个地区的第k个一级指标中的第j个二级指标经预处理后的指标值,n表示地区编号。
in, Represents the normalized weight of the j-th second-level indicator in the k-th first-level indicator of the i-th region, is the preprocessed index value of the j-th second-level indicator among the k-th first-level indicators in the i-th region, and n represents the region number.
(2)计算第k个一级指标中第j个二级指标的熵权:(2) Calculate the entropy weight of the j-th second-level indicator in the k-th first-level indicator:
(3)计算第k个一级指标的权重:每个一级指标的二级指标权重求和得到一级指标权重:(3) Calculate the weight of the kth first-level indicator: Sum the second-level indicator weights of each first-level indicator to get the first-level indicator weight:
步骤S5、计算碳中和指数,进行地区碳中和的综合评价。Step S5: Calculate the carbon neutrality index and conduct a comprehensive evaluation of regional carbon neutrality.
计算碳中和指数的公式如下:The formula for calculating the carbon neutrality index is as follows:
其中,Carbon Index
i表示第i个地区最终的碳中和指数,
表示第i个地区第k个一级指标中第j个二级指标经过正向化、归一化,但未经过对数化的指标值,v
(k)代表第k个一级指标经熵权法得到的权重,
表示第k个一级指标中的第j个二级指标经稀疏主成分赋权得到的权重。
Among them, Carbon Index i represents the final carbon neutrality index of the i-th region, Represents the index value of the j-th second-level indicator among the k-th first-level indicator in the i-th region that has been forwarded and normalized but not logarithmized. v (k) represents the entropy of the k-th first-level indicator. The weight obtained by the power method, Represents the weight obtained by sparse principal component weighting of the j-th second-level indicator in the k-th first-level indicator.
可根据比较计算出的碳中和指数大小,对不同地区的碳中和程度做出综合评价,指数值越高,代表碳中和完成度越好。A comprehensive evaluation of the degree of carbon neutrality in different regions can be made based on the calculated carbon neutrality index. The higher the index value, the better the completion of carbon neutrality.
本发明的优点和积极效果是:The advantages and positive effects of the present invention are:
本发明在现有碳中和指数指标体系的基础上,对指标赋权的方法进行改进, 引入稀疏主成分分析的方法,能够有效地筛选出指标池的重要指标,精简指标池,有效避免了庞大的指标体系存在的指标冗余的问题。同时,本发明在指标预处理过程中,将归一化的指标进行对数化处理;在指数合成过程中,使用几何加权的形式替代传统的线性代数加权。该种处理方式使得本发明得到的碳中和指数能够有效衡量指标间发展不平衡的问题。本发明取稀疏第一主成分作为二级指标权重,使用熵权法求解一级指标权重,通过两阶段权重融合的方法,使得指数结果更为稳健。Based on the existing carbon neutrality index index system, the present invention improves the index weighting method and introduces the method of sparse principal component analysis, which can effectively screen out important indicators of the indicator pool, streamline the indicator pool, and effectively avoid The huge indicator system has the problem of indicator redundancy. At the same time, the present invention performs logarithmic processing on the normalized index during the index preprocessing process; and uses geometric weighting to replace traditional linear algebra weighting during the index synthesis process. This processing method enables the carbon neutrality index obtained by the present invention to effectively measure the problem of unbalanced development among indicators. This invention takes the sparse first principal component as the secondary index weight, uses the entropy weight method to solve the primary index weight, and makes the index result more robust through a two-stage weight fusion method.
在本发明中,除非另有明确的规定和限定,术语“安装”、“相连”、“连接”、“固定”等术语应做广义理解,例如,可以是固定连接,也可以是可拆卸连接,或成一体;可以是机械连接,也可以是电连接或彼此可通讯;可以是直接相连,也可以通过中间媒介间接相连,可以是两个元件内部的连通或两个元件的相互作用关系,除非另有明确的限定。对于本领域的普通技术人员而言,可以根据具体情况理解上述术语在本发明中的具体含义。In the present invention, unless otherwise clearly stated and limited, the terms "installation", "connection", "connection", "fixing" and other terms should be understood in a broad sense. For example, it can be a fixed connection or a detachable connection. , or integrated; it can be mechanically connected, electrically connected or communicable with each other; it can be directly connected or indirectly connected through an intermediate medium; it can be the internal connection of two elements or the interaction between two elements, Unless otherwise expressly limited. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.
以上仅为说明本发明的实施方式,并不用于限制本发明,对于本领域的技术人员来说,凡在本发明的精神和原则之内,不经过创造性劳动所作的任何修改、等同替换、改进等,均应包含在本发明的保护范围之内。The above is only for illustrating the embodiments of the present invention and is not intended to limit the present invention. For those skilled in the art, any modifications, equivalent substitutions, and improvements within the spirit and principles of the present invention can be made without creative efforts. etc., should all be included in the protection scope of the present invention.
Claims (10)
- 一种基于稀疏对数主成分分析的碳中和综合评价方法,其特征在于,包括以下步骤:A carbon neutrality comprehensive evaluation method based on sparse logarithmic principal component analysis, which is characterized by including the following steps:根据碳排放情况采集行业信息作为评价指标,按类别对评价指标进行人工标引,利用人工标引的评价指标构建多维碳中和指标体系;所述多维碳中和指标体系包括一级指标和二级指标;Collect industry information based on carbon emissions as evaluation indicators, manually index the evaluation indicators by category, and use the manually indexed evaluation indicators to build a multi-dimensional carbon neutrality index system; the multi-dimensional carbon neutrality index system includes first-level indicators and second-level indicators. level indicators;对所述二级指标进行数据预处理;Perform data preprocessing on the secondary indicators;使用稀疏主成分分析的方法对所述二级指标进行赋权;Use sparse principal component analysis method to weight the secondary indicators;使用熵权法对所述一级指标进行赋权;Use the entropy weight method to weight the first-level indicators;计算碳中和指数,根据将所述碳中和指数进行地区碳中和的综合评价。Calculate the carbon neutrality index, and conduct a comprehensive evaluation of regional carbon neutrality based on the carbon neutrality index.
- 根据权利要求1所述的基于稀疏对数主成分分析的碳中和综合评价方法,其特征在于,所述一级指标用于表示分析维度;所述二级指标用于表示每一个分析维度下的具体分析指标。The carbon neutrality comprehensive evaluation method based on sparse logarithmic principal component analysis according to claim 1, characterized in that the first-level index is used to represent the analysis dimension; the second-level index is used to represent each analysis dimension. specific analysis indicators.
- 根据权利要求1所述的基于稀疏对数主成分分析的碳中和综合评价方法,其特征在于,对所述二级指标进行数据预处理包括:指标正向化、指标归一化和指标对数化。The carbon neutrality comprehensive evaluation method based on sparse logarithmic principal component analysis according to claim 1, characterized in that data preprocessing of the secondary indicators includes: indicator forwarding, indicator normalization and indicator pairing. Digitization.
- 根据权利要求3所述的基于稀疏对数主成分分析的碳中和综合评价方法,其特征在于,所述指标正向化,具体为:根据各二级指标对碳中和目标的正负面影响,判断所述二级指标是否高于预设阈值,若高于,则设置该二级指标为正向指标;否则,则设置该二级指标为负向指标。The carbon neutrality comprehensive evaluation method based on sparse logarithmic principal component analysis according to claim 3, characterized in that the indicators are positive, specifically: according to the positive and negative impacts of each secondary indicator on the carbon neutrality target , determine whether the secondary indicator is higher than the preset threshold. If it is higher, the secondary indicator is set as a positive indicator; otherwise, the secondary indicator is set as a negative indicator.
- 根据权利要求3所述的基于稀疏对数主成分分析的碳中和综合评价方法,其特征在于,所述指标归一化过程如下公式所示:The carbon neutrality comprehensive evaluation method based on sparse logarithmic principal component analysis according to claim 3, characterized in that the index normalization process is as follows:
- 根据权利要求3所述的基于稀疏对数主成分分析的碳中和综合评价方法,其特征在于,所述指标对数化过程如下公式所示:The carbon neutrality comprehensive evaluation method based on sparse logarithmic principal component analysis according to claim 3, characterized in that the logarithmic process of the index is as follows:
- 根据权利要求1所述的基于稀疏对数主成分分析的碳中和综合评价方法,其特征在于,所述使用稀疏主成分分析的方法对各维度二级指标进行赋权的过程包括:The carbon neutrality comprehensive evaluation method based on sparse logarithmic principal component analysis according to claim 1, characterized in that the process of using the sparse principal component analysis method to weight the secondary indicators of each dimension includes:记 为第i个地区的第k个一级指标中的第j个二级指标经预处理后的指标值,第k个指标维度所构成的指标矩阵为 第k个指标维度的各指标未归一化权重系数β,权重系数的求解过程如下: remember is the preprocessed index value of the j-th second-level indicator among the k-th first-level indicators in the i-th region, and the indicator matrix composed of the k-th indicator dimension is Each indicator of the kth indicator dimension has no normalized weight coefficient β. The solution process of the weight coefficient is as follows:s.t.α Tα=1 stα T α=1其中, λ和λ 1是调节参数,λ 1的选择对权重稀疏性取决定性作用; 是稀疏的第一主成分载荷系数;α表示单位常向量, 表示第k个指标所对应的单位化单位常向量经过归一化可得,第k个指标维度的各指标的权重系数为: in, λ and λ 1 are adjustment parameters, and the choice of λ 1 plays a decisive role in the sparsity of the weight; is the sparse first principal component loading coefficient; α represents the unit constant vector, The unit constant vector corresponding to the kth indicator can be obtained after normalization. The weight coefficient of each indicator in the kth indicator dimension is:
- 根据权利要求1所述的基于稀疏对数主成分分析的碳中和综合评价方法,其特征在于,所述使用熵权法对一级指标进行赋权,具体为:The carbon neutrality comprehensive evaluation method based on sparse logarithmic principal component analysis according to claim 1, characterized in that the entropy weight method is used to weight the first-level indicators, specifically:计算第k个一级指标中第j个二级指标的信息熵;Calculate the information entropy of the j-th secondary indicator among the k-th primary indicator;利用所述二级指标的信息熵计算第k个一级指标中第j个二级指标的熵权;Calculate the entropy weight of the j-th secondary indicator in the k-th primary indicator using the information entropy of the secondary indicator;利用所述二级指标的熵权计算第k个一级指标的权重。The weight of the kth primary indicator is calculated using the entropy weight of the secondary indicator.
- 根据权利要求8所述的基于稀疏对数主成分分析的碳中和综合评价方法,其特征在于,所述计算第k个一级指标的权重的公式为:The carbon neutrality comprehensive evaluation method based on sparse logarithmic principal component analysis according to claim 8, characterized in that the formula for calculating the weight of the kth first-level indicator is:其中 为第k个一级指标中第j个二级指标的熵权, 的计算公式为: in is the entropy weight of the j-th secondary indicator among the k-th primary indicator, The calculation formula is:其中 为第k个一级指标中第j个二级指标的信息熵, 的计算公式为: in is the information entropy of the j-th secondary indicator among the k-th primary indicator, The calculation formula is:
- 根据权利要求1所述的基于稀疏对数主成分分析的碳中和综合评价方法,其特征在于,所述计算碳中和指数,具体为:The carbon neutrality comprehensive evaluation method based on sparse logarithmic principal component analysis according to claim 1, characterized in that the calculated carbon neutrality index is specifically:其中,Carbon Index i表示第i个地区最终的碳中和指数, 表示第i个地区第k个一级指标中第j个二级指标经过正向化、归一化,但未经过对数化的指标值,v (k)代表第k个一级指标经熵权法得到的权重, 表示第k个一级指标中的第j个二级指标经稀疏主成分赋权得到的权重。 Among them, Carbon Index i represents the final carbon neutrality index of the i-th region, Represents the index value of the j-th second-level indicator among the k-th first-level indicator in the i-th region that has been forwarded and normalized but not logarithmized. v (k) represents the entropy of the k-th first-level indicator. The weight obtained by the power method, Represents the weight obtained by sparse principal component weighting of the j-th second-level indicator in the k-th first-level indicator.
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