CN114820262A - Carbon dioxide emission change and emission contribution evaluation method and device - Google Patents

Carbon dioxide emission change and emission contribution evaluation method and device Download PDF

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CN114820262A
CN114820262A CN202210754259.XA CN202210754259A CN114820262A CN 114820262 A CN114820262 A CN 114820262A CN 202210754259 A CN202210754259 A CN 202210754259A CN 114820262 A CN114820262 A CN 114820262A
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carbon dioxide
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dioxide emission
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王昊
王宇翔
周令泉
廖通逵
宗继彪
刘福权
刘梦晓
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Aerospace Hongtu Information Technology Co Ltd
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Abstract

The invention provides a method and a device for evaluating carbon dioxide emission change and emission contribution, which relate to the technical field of carbon dioxide emission evaluation and comprise the following steps: acquiring target data of a target area in a preset time period, wherein the target data comprises: remote sensing image data, meteorological data and attribute data of social and economic characteristic attributes; carrying out normalization processing on the meteorological data to obtain normalized data, and processing the remote sensing image data and the normalized data by utilizing a multivariate linear regression model to obtain carbon dioxide emission change data; calculating the change value of the annual emission factor of the carbon dioxide in the target area based on the target data and the normalized data; constructing a random forest model based on the change value and attribute data of the annual emission factor of the carbon dioxide; based on the random forest model, the carbon dioxide emission contribution degree corresponding to the social and economic characteristic attribute is determined, and the technical problem that the emission influence of carbon dioxide is difficult to accurately evaluate in the prior art is solved.

Description

Carbon dioxide emission change and emission contribution evaluation method and device
Technical Field
The invention relates to the technical field of carbon dioxide emission evaluation, in particular to a method and a device for evaluating carbon dioxide emission change and emission contribution.
Background
At present, the accounting for the emission amount of CO2 mainly comprises a bottom-up emission inventory method and a top-down atmospheric concentration observation data inversion greenhouse gas flux two methods. An emission manifest method is a carbon emission estimation method proposed by IPCC (international commission on climate change between governments of united states), and according to a carbon emission manifest list, activity data and an emission factor are constructed for each emission source, and the product of the activity data and the emission factor is used as an estimation value of the carbon emission amount of the emission item. The satellite remote sensing observation of the atmospheric concentration has the advantages of objective, continuous, stable, large-range and repeated observation, and becomes a new generation and internationally recognized global carbon check method. In 2016, the new Delhi declaration emphasizes that space-borne atmosphere monitoring can be used as a supplementary system for estimating national autonomous contribution INDC (Integrated national defined controls), and the international committee on earth observation of satellites also explicitly proposes to form constellation business operation in 2025, and supports 2028-year global carbon inventory. The 'bottom-up' emission inventory method has the problem that the workload is large and the full coverage cannot be achieved, but the influence of macroscopic policy factors, meteorological factors and the like on the CO2 emission is generally not considered in the current top-down remote sensing inspection mode, and the emission importance of the emission influence attribute is not well evaluated.
No effective solution has been proposed to the above problems.
Disclosure of Invention
In view of the above, the present invention provides a method and an apparatus for evaluating emission change and emission contribution of carbon dioxide, so as to alleviate the technical problem that it is difficult to accurately evaluate the emission influence of carbon dioxide in the prior art.
In a first aspect, an embodiment of the present invention provides a method for evaluating a change in carbon dioxide emission and an emission contribution, including: acquiring target data of a target area in a preset time period, wherein the target data comprises: remote sensing image data, meteorological data and attribute data of social and economic characteristic attributes; carrying out normalization processing on the meteorological data to obtain normalized data, and processing the remote sensing image data and the normalized data by utilizing a multivariate linear regression model to obtain carbon dioxide emission change data; calculating an annual emission factor variation value of carbon dioxide in the target area based on the target data and the normalized data; constructing a random forest model based on the carbon dioxide annual emission factor change value and the attribute data; and determining the carbon dioxide emission contribution degree corresponding to the socioeconomic characteristic attribute based on the random forest model.
Further, the socioeconomic feature attributes include: GDP, a first industrial output value, a second industrial output value, a third industrial output value, an area of a built-up area, a land urbanization rate, a population scale, a population density of the built-up area, a green land area of the built-up area, a gas supply coverage rate, a heat supply volume rate, a living density, a road density, an owned quantity of public transportation means per ten thousand and a motor vehicle reserved quantity per capita.
Further, constructing a random forest model based on the carbon dioxide annual emission factor variation value and the attribute data, including: constructing a data set based on the carbon dioxide annual emission factor change value and the attribute data; performing a replacement random sampling on the data sets to obtain a preset number of sampling sets; and constructing the random forest model by utilizing a preset number of sampling sets.
Further, calculating an annual emission factor change value of carbon dioxide for the target area based on the target data and the normalized data, comprising: fitting the target data and the normalized data by using a fitting formula to determine a carbon dioxide emission factor of each pixel point in the target area in the preset time period; calculating the average value of the carbon dioxide emission factors of each pixel point in the preset time period based on the carbon dioxide emission factors of each pixel point in the preset time period; calculating a carbon dioxide emission factor accumulated value of each year of the target area based on the average value; and calculating the annual carbon dioxide emission factor change value based on the accumulated carbon dioxide emission factor value of each year of the target area.
Further, determining a carbon dioxide emission contribution degree corresponding to the socioeconomic characteristic attribute based on the random forest model, including: calculating a kini index corresponding to each socioeconomic characteristic attribute based on the random forest model; calculating initial carbon dioxide emission contribution degrees corresponding to all socioeconomic characteristic attributes based on the Gini index; and carrying out normalization processing on the initial carbon dioxide emission contribution degree to obtain the carbon dioxide emission contribution degree.
Further, the method further comprises: and sequencing the carbon dioxide emission contribution degrees, and determining the carbon dioxide emission influence grade corresponding to the socioeconomic characteristic attribute.
Further, determining the carbon dioxide emission influence classification corresponding to the socioeconomic characteristic attribute comprises the following steps: calculating the variance of the carbon dioxide emission contribution degree and the sum of the variances; calculating the ratio of the variance corresponding to each social and economic characteristic attribute to the sum of the variances; and determining the carbon dioxide emission influence classification corresponding to the socioeconomic characteristic attribute based on the ratio.
In a second aspect, an embodiment of the present invention further provides a device for evaluating a change in carbon dioxide emission and an emission contribution, including: the device comprises an acquisition unit, a processing unit, a calculation unit, a construction unit and a determination unit, wherein the acquisition unit is used for acquiring target data of a target area in a preset time period, and the target data comprises: remote sensing image data, meteorological data and attribute data of social and economic characteristic attributes; the processing unit is used for carrying out normalization processing on the meteorological data to obtain normalized data, and processing the remote sensing image data and the normalized data by utilizing a multivariate linear regression model to obtain carbon dioxide emission change data; the calculation unit is used for calculating the change value of the annual emission factor of the carbon dioxide in the target area based on the target data and the normalized data; the construction unit is used for constructing a random forest model based on the carbon dioxide annual emission factor change value and the attribute data; and the determining unit is used for determining the carbon dioxide emission contribution degree corresponding to the socioeconomic characteristic attribute based on the random forest model.
In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory and a processor, where the memory is used to store a program that supports the processor to execute the method in the first aspect, and the processor is configured to execute the program stored in the memory.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored.
In the embodiment of the present invention, target data of a target area in a preset time period is acquired, where the target data includes: remote sensing image data, meteorological data and attribute data of social and economic characteristic attributes; processing the remote sensing image data by using a multivariate linear regression model to obtain carbon dioxide emission change data, and performing normalization processing on meteorological data to obtain normalized data; calculating the change value of the annual carbon dioxide emission factor of the target area based on the target data and the normalized data; constructing a random forest model based on the change value and attribute data of the annual emission factor of the carbon dioxide; based on the random forest model, the carbon dioxide emission contribution degree corresponding to the social and economic characteristic attribute is determined, the purpose of long-term and accurate evaluation of carbon dioxide emission is achieved, the technical problem that in the prior art, accurate evaluation of the emission influence of carbon dioxide is difficult to carry out is solved, and therefore the technical effect of improving the accuracy of the carbon dioxide emission influence evaluation is achieved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a method for assessing carbon dioxide emissions and emissions contributions according to an embodiment of the invention;
FIG. 2 is a schematic diagram of an apparatus for evaluating carbon dioxide emission variation and emission contribution according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first embodiment is as follows:
in accordance with an embodiment of the present invention, there is provided an embodiment of a method for carbon dioxide emissions variation and emissions contribution estimation, it being noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system, such as a set of computer-executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than presented herein.
Fig. 1 is a flow chart of a method for evaluating carbon dioxide emission variation and emission contribution according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step S102, acquiring target data of a target area in a preset time period, wherein the target data comprises: remote sensing image data, meteorological data and attribute data of social and economic characteristic attributes;
the remote-sensing image data may be GOSAT L2 CO2 data, and the meteorological data may be ERA5 data.
The socioeconomic characteristics described above include: GDP, a first industrial output value, a second industrial output value, a third industrial output value, an area of a built-up area, a land urbanization rate, a population scale, a population density of the built-up area, a green land area of the built-up area, a gas supply coverage rate, a heat supply volume rate, a living density, a road density, an owned quantity of public transportation means per ten thousand and a motor vehicle reserved quantity per capita.
Step S104, carrying out normalization processing on the meteorological data to obtain normalized data, and processing the remote sensing image data and the normalized data by utilizing a multiple linear regression model to obtain carbon dioxide emission change data;
firstly, normalization processing is carried out on meteorological data, and a normalization processing formula is as follows:
Figure M_220613152621436_436304001
wherein, in the step (A),
Figure M_220613152621499_499837002
in order to be the meteorological data,
Figure M_220613152621516_516871003
to normalize the data.
Then inputting the normalized data and the remote sensing image data into a multiple linear regression model, wherein the expression of the multiple linear regression model is as follows:
Figure M_220613152621551_551524001
wherein, in the step (A),
Figure M_220613152621645_645785002
the CO2 index after log deformation (normal distribution after deformation);
Figure F_220613152620369_369412001
is the year;
Figure F_220613152620510_510048002
influence factors for representing emission reduction policies of different years;
Figure F_220613152620638_638942003
to eliminate the emission levels after various influencing factors.
Figure M_220613152621692_692659001
The calculation formula of (a) is as follows:
Figure M_220613152621723_723897001
wherein, due to the common CO in autumn and winter 2 The concentration is higher than that in spring and summer, so that the trigonometric functions with the use period of 1 year and 1/2 years are used for expressing CO 2 The discharge is changed in a seasonal way,
Figure M_220613152621788_788871001
、
Figure M_220613152621804_804467002
、
Figure M_220613152621835_835717003
、
Figure M_220613152621866_866974004
is a factor corresponding to sine and cosine.
Then, the data are normalized to calculate
Figure M_220613152621898_898223001
，
Figure M_220613152621929_929476002
The expression of (c) is:
Figure M_220613152621947_947995001
。
Figure M_220613152622192_192657002
ERA5 meteorological data is used, wherein,
Figure M_220613152622223_223897003
for 10 meter wind speed data (from latitudinal (u) and longitudinal (v) wind speeds:
Figure M_220613152622255_255187004
) Meanwhile, in order to obtain normally distributed 10 m wind speed data,
Figure M_220613152622286_286410005
log deformation is carried out after 3 m/s is added;
Figure M_220613152622317_317658006
2 meters temperature data;
Figure M_220613152622333_333300007
the total precipitation is obtained;
Figure M_220613152622366_366484008
、
Figure M_220613152622382_382099009
、
Figure M_220613152622413_413387010
、
Figure M_220613152622444_444608011
、
Figure M_220613152622460_460255012
is a factor of the corresponding meteorological data.
By adopting multiple linear regression, macroscopic policy factors, meteorological factors and emission periodicity are fully considered, so that the influence of natural variation, seasonal alternation and the 'weekend effect' of the human society on the calculation result is eliminated, and finally, a long-term CO2 emission variation with less interference is obtained, and a more accurate emission influence attribute importance analysis result is obtained.
Step S106, calculating the annual emission factor change value of the carbon dioxide in the target area based on the target data and the normalized data;
step S108, constructing a random forest model based on the carbon dioxide annual emission factor change value and the attribute data;
and S110, determining the carbon dioxide emission contribution degree corresponding to the socioeconomic characteristic attribute based on the random forest model.
In the embodiment of the present invention, target data of a target area in a preset time period is acquired, where the target data includes: remote sensing image data, meteorological data and attribute data of social and economic characteristic attributes; processing the remote sensing image data by using a multivariate linear regression model to obtain carbon dioxide emission change data, and performing normalization processing on meteorological data to obtain normalized data; calculating the change value of the annual emission factor of the carbon dioxide in the target area based on the target data and the normalized data; constructing a random forest model based on the change value and attribute data of the annual emission factor of the carbon dioxide; based on the random forest model, the carbon dioxide emission contribution degree corresponding to the social and economic characteristic attribute is determined, the purpose of long-term and accurate evaluation of carbon dioxide emission is achieved, the technical problem that in the prior art, accurate evaluation of the emission influence of carbon dioxide is difficult to carry out is solved, and therefore the technical effect of improving the accuracy of the carbon dioxide emission influence evaluation is achieved.
In the embodiment of the present invention, step S106 includes the following steps:
step S11, fitting the target data and the normalized data by using a fitting formula, and determining a carbon dioxide emission factor of each pixel point in the target area in the preset time period;
step S12, calculating the average value of the carbon dioxide emission factors of each pixel point in the preset time period based on the carbon dioxide emission factors of each pixel point in the preset time period;
step S13, calculating the carbon dioxide emission factor accumulated value of each year in the target area based on the average value;
step S14, calculating the annual carbon dioxide emission factor variation value based on the accumulated value of the annual carbon dioxide emission factors of the target area.
In the embodiment of the invention, a fitting formula is firstly used for calculating the CO2 emission factor of each pixel point in the target area
Figure M_220613152622491_491484001
（i=0,2，...k)。
Then, calculating the average value of the CO2 emission factors of each pixel point in the target area, wherein the calculation formula is as follows:
Figure M_220613152622522_522731001
next, the cumulative emission factor of the target region is calculated in the time dimension.
Accumulation is usually done year by year, but accumulation may also be done quarterly or monthly, depending on the regional data statistics dimension.
It is assumed that the annual emission factor is listed as
Figure M_220613152622557_557400001
Then, the cumulative emission factor value in the j-th year is:
Figure M_220613152622589_589142002
。
and finally, calculating the change value of the annual emission factor of the carbon dioxide, wherein the calculation formula is as follows:
Figure M_220613152622636_636027001
。
in the embodiment of the present invention, step S108 includes the following steps:
a step S21 of constructing a data set based on the carbon dioxide annual emission factor variation value and the attribute data;
step S22, performing replacement random sampling on the data set to obtain a preset number of sampling sets;
and step S23, constructing the random forest model by using a preset number of sampling sets.
In an embodiment of the present invention, a data set is first constructed using carbon dioxide annual emission factor variation values and attribute data.
And (3) performing m (m is less than or equal to N, and N is the number of samples in the data set) times of replaced random sampling on the data set to obtain a sampling set, and repeating the T times to obtain T sampling sets.
For each sampling set, randomly selecting k attributes from all socioeconomic characteristic attributes, selecting the optimal segmentation attribute as a node to establish a CART model, and finally establishing a random forest model with T CART models by utilizing T sampling sets.
It should be noted that, in the following description,
Figure M_220613152622667_667262001
and d is all socioeconomic characteristic attributes of the sample.
And (3) obtaining a final result by adopting a combination strategy of an averaging method, wherein the formula is as follows:
Figure M_220613152622698_698528001
wherein the content of the first and second substances,
Figure M_220613152622748_748310001
the result is calculated for the ith decision tree,
Figure M_220613152622764_764425002
and outputting the result for the random forest finally.
In the embodiment of the present invention, step S110 includes the following steps:
step S31, calculating a kini index corresponding to each socioeconomic characteristic attribute based on the random forest model;
step S32, calculating initial carbon dioxide emission contribution degrees corresponding to the socioeconomic characteristic attributes based on the Gini index;
step S33, performing normalization processing on the initial carbon dioxide emission contribution degree to obtain the carbon dioxide emission contribution degree.
In the embodiment of the invention, if the random forest model is input
Figure M_220613152622795_795680001
(j =1,2.. m) socioeconomic feature attributes, and each feature is calculated in turn
Figure M_220613152622811_811310002
Gini index of
Figure M_220613152622842_842561003
I.e. first
Figure M_220613152622873_873809004
Characterized by random forestThe average amount of change in node fragmentation purity in all decision trees of the model.
The Gini index is calculated as:
Figure M_220613152622905_905066001
；
wherein the content of the first and second substances,
Figure M_220613152622953_953357001
is shown as
Figure M_220613152622985_985153002
The number of the categories is one,
Figure M_220613152623000_000770003
representing nodes
Figure M_220613152623031_031513004
Middle class
Figure M_220613152623047_047639005
The ratio of the active ingredients to the total amount of the active ingredients. That is, the random slave node
Figure M_220613152623063_063272006
Two samples are randomly drawn, with the class labels indicating the probability of inconsistency.
Feature(s)
Figure M_220613152623094_094516001
At a node
Figure M_220613152623110_110161002
Of importance, i.e. nodes
Figure M_220613152623142_142340003
The Gini index change before and after branching was:
Figure M_220613152623158_158489004
；
wherein the content of the first and second substances,
Figure M_220613152623205_205356001
and
Figure M_220613152623236_236602002
representing two new nodes before and after the branch respectively
Figure M_220613152623252_252224003
And (4) index.
If, the characteristics
Figure M_220613152623283_283485001
In decision trees
Figure M_220613152623299_299114002
Node appearing in as a set
Figure M_220613152623330_330362003
Then, then
Figure M_220613152623348_348407004
In the first place
Figure M_220613152623380_380161005
The importance of the particular tree is
Figure M_220613152623395_395781006
。
Suppose that
Figure M_220613152623427_427030001
All of them share
Figure M_220613152623458_458300002
(iii) trees, then the initial carbon dioxide emission contribution is:
Figure M_220613152623473_473897001
and normalizing the initial carbon dioxide emission contribution degree by using the following formula to obtain the carbon dioxide emission contribution degree.
Figure M_220613152623520_520783001
。
In an embodiment of the present invention, the method further comprises:
and S112, sequencing the carbon dioxide emission contribution degrees, and determining the carbon dioxide emission influence grades corresponding to the socioeconomic characteristic attributes.
Specifically, the variance and the sum of the variances of the carbon dioxide emission contribution degrees are calculated;
calculating the ratio of the variance corresponding to each social and economic characteristic attribute to the sum of the variances;
and determining the carbon dioxide emission influence classification corresponding to the socioeconomic characteristic attribute based on the ratio.
Firstly, the contribution degrees of carbon dioxide emission are sequenced, and then the carbon dioxide emission influence classification corresponding to the socioeconomic characteristic attributes is determined by referring to a natural breakpoint method classification method.
The specific process is as follows:
calculating the variance SDAM of the raw data
Figure M_220613152623553_553952001
In the formula (I), the compound is shown in the specification,
Figure M_220613152623601_601357001
n is the emission impact attribute number, which is the mean of all importance scores.
Calculating the sum of all combined variances SDCM
Generally, the carbon dioxide emission influence corresponding to the economic characteristic attribute is classified into three stages: important, generally, not important, i.e. k = 3. Or may be classified into 4 classes or 5 classes according to service needs. Constructing all classification combinations according to k =3, wherein the number of the combinations is M, and calculating the sum of all the combination variances, wherein the formula is as follows:
Figure M_220613152623632_632623001
in the formula (I), the compound is shown in the specification,
Figure M_220613152623679_679462001
is the mean of all VIM values in the ith classification,
Figure M_220613152623710_710719002
is the number of VIM values in the ith classification.
Then, calculate "goodness of fit of variance" (GVF)
Figure M_220613152623743_743409001
The GVF takes the combination of the maximum values as the final classification result, and the three categories are important, generally not important in turn according to the VIM from large to small.
The embodiment of the invention combines socio-economic acceleration data, such as GDP, total production value of first, second and third industries, population structure, total power generation, urbanization rate and the like acceleration, uses random forest regression, and aims at CO for each socio-economic index 2 The influence of the change in emission was evaluated.
Furthermore, the embodiment of the invention has flexibility and expandability. And different time scales and different space scale evaluations are supported. The method can be applied to different research scales from counties to nationwide, and main driving force for emission can be searched according to different socioeconomic conditions and emission conditions of different regions, so that technical support is provided for policy making of decision makers in aspects of energy conservation, emission reduction, adjustment of socioeconomic structures and the like. Meanwhile, the refining of emission influence attributes (factors) is supported, for example, the first industry is speeded up to further subdivide the industry, so that more refined CO aiming at the subdividing industry can be obtained 2 Evaluation of the influence of the change in emissions.
The emission impact attribute importance score (VIM) adopts a clustering classification evaluation method instead of a fixed threshold classification method, so that the situation that similar VIM values are distributed to different grades is effectively avoided, and the situation that an improper classification result misleads a decision maker is avoided.
The multivariate regression model can be used independently for evaluating the relative change of CO2 emission in a region, and the evaluation of the change of CO2 emission from the highest time resolution to the day can be realized according to different time resolutions of remote sensing data.
Example two:
the embodiment of the present invention further provides a device for evaluating carbon dioxide emission variation and emission contribution, which is used for executing the method for evaluating carbon dioxide emission variation and emission contribution provided by the above-mentioned content of the embodiment of the present invention, and the following is a specific description of the device for evaluating carbon dioxide emission variation and emission contribution provided by the embodiment of the present invention.
As shown in fig. 2, fig. 2 is a schematic view of the carbon dioxide emission variation and emission contribution evaluation device, which includes: an acquisition unit 10, a processing unit 20, a calculation unit 30, a construction unit 40 and a determination unit 50.
The acquiring unit 10 is configured to acquire target data of a target area within a preset time period, where the target data includes: remote sensing image data, meteorological data and attribute data of social and economic characteristic attributes;
the processing unit 20 is configured to perform normalization processing on the meteorological data to obtain normalized data, and process the remote sensing image data and the normalized data by using a multiple linear regression model to obtain carbon dioxide emission change data;
the calculating unit 30 is configured to calculate a carbon dioxide annual emission factor variation value of the target area based on the target data and the normalized data;
the construction unit 40 is configured to construct a random forest model based on the carbon dioxide annual emission factor variation value and the attribute data;
the determining unit 50 is configured to determine, based on the random forest model, a carbon dioxide emission contribution degree corresponding to the socioeconomic characteristic attribute.
In the embodiment of the present invention, target data of a target area in a preset time period is acquired, where the target data includes: remote sensing image data, meteorological data and attribute data of social and economic characteristic attributes; processing the remote sensing image data by using a multivariate linear regression model to obtain carbon dioxide emission change data, and performing normalization processing on meteorological data to obtain normalized data; calculating the change value of the annual emission factor of the carbon dioxide in the target area based on the target data and the normalized data; constructing a random forest model based on the change value and attribute data of the annual carbon dioxide emission factor; based on the random forest model, the carbon dioxide emission contribution degree corresponding to the social and economic characteristic attribute is determined, the purpose of long-term and accurate evaluation of carbon dioxide emission is achieved, the technical problem that in the prior art, accurate evaluation of the emission influence of carbon dioxide is difficult to carry out is solved, and therefore the technical effect of improving the accuracy of the carbon dioxide emission influence evaluation is achieved.
Example three:
an embodiment of the present invention further provides an electronic device, including a memory and a processor, where the memory is used to store a program that supports the processor to execute the method described in the first embodiment, and the processor is configured to execute the program stored in the memory.
Referring to fig. 3, an embodiment of the present invention further provides an electronic device 100, including: a processor 60, a memory 61, a bus 62 and a communication interface 63, wherein the processor 60, the communication interface 63 and the memory 61 are connected through the bus 62; the processor 60 is arranged to execute executable modules, such as computer programs, stored in the memory 61.
The Memory 61 may include a high-speed Random Access Memory (RAM) and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 63 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like may be used.
The bus 62 may be an ISA bus, PCI bus, EISA bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 3, but this does not indicate only one bus or one type of bus.
The memory 61 is used for storing a program, the processor 60 executes the program after receiving an execution instruction, and the method executed by the apparatus defined by the flow process disclosed in any of the foregoing embodiments of the present invention may be applied to the processor 60, or implemented by the processor 60.
The processor 60 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 60. The Processor 60 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory 61, and the processor 60 reads the information in the memory 61 and, in combination with its hardware, performs the steps of the above method.
Example four:
the embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program performs the steps of the method in the first embodiment.
In addition, in the description of the embodiments of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (10)

1. A method for assessing carbon dioxide emission variation and emission contribution is characterized by comprising the following steps:
acquiring target data of a target area in a preset time period, wherein the target data comprises: remote sensing image data, meteorological data and attribute data of social and economic characteristic attributes;
carrying out normalization processing on the meteorological data to obtain normalized data, and processing the remote sensing image data and the normalized data by utilizing a multivariate linear regression model to obtain carbon dioxide emission change data;
calculating an annual emission factor variation value of carbon dioxide in the target area based on the target data and the normalized data;
constructing a random forest model based on the carbon dioxide annual emission factor change value and the attribute data;
and determining the carbon dioxide emission contribution degree corresponding to the socioeconomic characteristic attribute based on the random forest model.
2. The method of claim 1,
the socioeconomic characteristics attributes include: GDP, a first industrial output value, a second industrial output value, a third industrial output value, an area of a built-up area, a land urbanization rate, a population scale, a population density of the built-up area, a green land area of the built-up area, a gas supply coverage rate, a heat supply volume rate, a living density, a road density, an owned quantity of public transportation means per ten thousand and a motor vehicle reserved quantity per capita.
3. The method of claim 1, wherein calculating an annual emission factor change value for carbon dioxide for the target area based on the target data and the normalized data comprises:
fitting the target data and the normalized data by using a fitting formula to determine a carbon dioxide emission factor of each pixel point in the target area within a preset time period;
calculating the average value of the carbon dioxide emission factors of each pixel point in the preset time period based on the carbon dioxide emission factors of each pixel point in the preset time period;
calculating a carbon dioxide emission factor accumulated value of each year of the target area based on the average value;
and calculating the annual carbon dioxide emission factor change value based on the accumulated carbon dioxide emission factor value of each year of the target area.
4. The method of claim 1, wherein constructing a random forest model based on the carbon dioxide annual emission factor variation value and the attribute data comprises:
constructing a data set based on the carbon dioxide annual emission factor change value and the attribute data;
performing a replacement random sampling on the data sets to obtain a preset number of sampling sets;
and constructing the random forest model by utilizing a preset number of sampling sets.
5. The method as claimed in claim 4, wherein determining the carbon dioxide emission contribution degree corresponding to the socioeconomic characteristic attribute based on the random forest model comprises:
calculating a kini index corresponding to each socioeconomic characteristic attribute based on the random forest model;
calculating initial carbon dioxide emission contribution degrees corresponding to all socioeconomic characteristic attributes based on the Gini index;
and carrying out normalization processing on the initial carbon dioxide emission contribution degree to obtain the carbon dioxide emission contribution degree.
6. The method of claim 1, further comprising:
and sequencing the carbon dioxide emission contribution degrees, and determining the carbon dioxide emission influence grade corresponding to the socioeconomic characteristic attribute.
7. The method of claim 6, wherein determining the carbon dioxide emission impact rating for the socio-economic trait attribute comprises:
calculating the variance of the carbon dioxide emission contribution degree and the sum of the variances;
calculating the ratio of the variance corresponding to each social and economic characteristic attribute to the sum of the variances;
and determining the carbon dioxide emission influence classification corresponding to the socioeconomic characteristic attribute based on the ratio.
8. A carbon dioxide emission variation and emission contribution evaluation device, comprising: an acquisition unit, a processing unit, a calculation unit, a construction unit and a determination unit, wherein,
the acquiring unit is configured to acquire target data of a target area within a preset time period, where the target data includes: remote sensing image data, meteorological data and attribute data of social and economic characteristic attributes;
the processing unit is used for carrying out normalization processing on the meteorological data to obtain normalized data, and processing the remote sensing image data and the normalized data by utilizing a multivariate linear regression model to obtain carbon dioxide emission change data;
the calculation unit is used for calculating the change value of the annual emission factor of the carbon dioxide in the target area based on the target data and the normalized data;
the construction unit is used for constructing a random forest model based on the carbon dioxide annual emission factor change value and the attribute data;
and the determining unit is used for determining the carbon dioxide emission contribution degree corresponding to the socioeconomic characteristic attribute based on the random forest model.
9. An electronic device comprising a memory for storing a program that enables a processor to perform the method of any of claims 1 to 7 and a processor configured to execute the program stored in the memory.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of the claims 1 to 7.
CN202210754259.XA 2022-06-30 2022-06-30 Carbon dioxide emission change and emission contribution evaluation method and device Pending CN114820262A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115436570A (en) * 2022-08-25 2022-12-06 二十一世纪空间技术应用股份有限公司 Carbon dioxide concentration remote sensing monitoring method and device based on multivariate data
CN115884009A (en) * 2023-03-02 2023-03-31 四川君迪能源科技有限公司 Remote real-time monitoring method, device and system for carbon dioxide emission
CN116189833A (en) * 2023-04-20 2023-05-30 国高材高分子材料产业创新中心有限公司 Method and device for calculating carbon emission amount of polymer material and product thereof

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115436570A (en) * 2022-08-25 2022-12-06 二十一世纪空间技术应用股份有限公司 Carbon dioxide concentration remote sensing monitoring method and device based on multivariate data
CN115884009A (en) * 2023-03-02 2023-03-31 四川君迪能源科技有限公司 Remote real-time monitoring method, device and system for carbon dioxide emission
CN115884009B (en) * 2023-03-02 2023-05-23 四川君迪能源科技有限公司 Remote real-time monitoring method, device and system for carbon dioxide emission
CN116189833A (en) * 2023-04-20 2023-05-30 国高材高分子材料产业创新中心有限公司 Method and device for calculating carbon emission amount of polymer material and product thereof
CN116189833B (en) * 2023-04-20 2023-08-18 国高材高分子材料产业创新中心有限公司 Method and device for calculating carbon emission amount of polymer material and product thereof

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