Centralized photovoltaic power station carbon emission reduction and ecological system carbon income and expenditure comprehensive evaluation method

Abstract

The invention discloses a centralized photovoltaic power station carbon emission reduction and ecological system carbon income and expenditure comprehensive evaluation method. The method comprises the following steps: determining an influence area of a photovoltaic power station based on a remote sensing image; calculating the net primary productivity of the influence area, and constructing a monthly / year-by-year productivity time sequence B of the influence area; calculating net ecosystem productivity # imgabs0 # according to productivity data and soil respiration consumption of the influence area; inputting remote sensing attribute variables and climatic factors of the influence area into a productivity prediction model to obtain net ecosystem productivity # imgabs1 #; fusing the # imgabs2 # and the # imgabs3 # by adopting a weighted average fusion method to generate a productivity fusion value; based on the fusion value, calculating the carbon income and expenditure of the influence area in a set time range; calculating the carbon emission reduction of the photovoltaic power station based on the annual average generating capacity and the power marginal emission factor of the research time period; calculating the proportion of the carbon income and expenditure in the total carbon benefit, and taking the proportion as an ecological contribution index ECCR; the ECCR is positive, the larger the ECCR is, the stronger the carbon sequestration capacity of the ecological system is, and when the ECCR is negative, the system is a carbon source.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological and environmental effects of energy development, and in particular to a comprehensive assessment method for carbon emission reduction in centralized photovoltaic power stations and carbon budget of ecosystems. Background Art

[0002] The environmental challenges posed by global climate change are becoming increasingly severe, and promoting a transition to a clean, low-carbon energy structure has become an international consensus. Against this backdrop, photovoltaic power generation, with its advantages such as advanced technology, rapidly declining costs, and widespread resource availability, is becoming a major force in this energy transition. By replacing fossil fuels such as coal with clean power generation, photovoltaic power stations can reduce greenhouse gas emissions and offer significant potential for carbon reduction.

[0003] Photovoltaic power stations have demonstrated significant advantages in promoting energy transition and carbon emission reduction, but the potential impact of their large-scale construction on ecosystems cannot be ignored. Photovoltaic projects are often located in areas with rich radiation conditions but relatively fragile ecosystems, which are very sensitive to subtle changes in land cover. Generally speaking, construction activities such as land leveling and infrastructure construction during the construction period of photovoltaic power stations will cause damage to the original vegetation and trigger short-term carbon source release; during the operation period, the photovoltaic array will respond to changes in environmental factors such as light, temperature and humidity, which will trigger a dynamic response of the carbon cycle process and show obvious regional differences: in arid or semi-arid areas, the shading effect helps to alleviate high-temperature evaporation, maintain soil moisture, and promote vegetation recovery; while in humid areas, excessive shading may inhibit photosynthesis and weaken carbon fixation capacity.

[0004] It can be seen that the benefits of photovoltaic power stations are not only reflected in carbon emission reduction, but also involve the complex response of the ecosystem and have multidimensional characteristics. At present, there is still a lack of systematic evaluation and quantification methods for the carbon balance effect brought about by vegetation changes caused by photovoltaics, which restricts the scientific understanding of its ecological role and the accurate reflection of its policy value. The reason behind this kind of systematic lack is mainly due to the limitations of existing research in three aspects: first, the limitation of research perspective. Currently, most of the focus is on the physical changes in vegetation cover, ignoring the deep-seated relationship between it and carbon balance; second, the insufficient application of technical means. The lack of depth in the application of information technologies such as remote sensing and big data limits the ability of refined dynamic analysis; finally, the lack of regional adaptability. Existing results are mostly based on case studies in a single region, which is difficult to generalize to carbon balance assessment in diverse ecological environments.

[0005] Due to the limitations of the above-mentioned assessment methods, during the site selection phase, due to the lack of coordinated consideration of carbon emission reduction and ecological carbon balance, areas with high ecological restoration potential and strong comprehensive carbon benefits may be mistakenly excluded, or ecologically sensitive areas with limited carbon contribution may be prioritized for development, causing greater damage to the ecological environment; during the design phase, if the layout of photovoltaic arrays and green areas is not optimized in combination with a comprehensive carbon balance assessment, it is easy to cause a separation between power generation and carbon balance functions, thereby reducing the overall carbon benefit (carbon benefit refers to the ecological and economic benefits generated by measures such as reducing carbon emissions and developing a low-carbon economy, aimed at promoting sustainable development; ecological benefits include mitigating climate change and extreme weather, enhancing ecological carbon fixation capacity and protecting ecological diversity); during the operation phase, there is a lack of continuous monitoring and assessment of the dynamic effects of carbon emission reduction and ecosystem carbon balance, and it is impossible to identify carbon benefit deviations in a timely manner. Summary of the Invention

[0006] In response to the above-mentioned deficiencies in the existing technology, the comprehensive assessment method for carbon emission reduction of centralized photovoltaic power stations and carbon balance of ecosystems provided by the present invention can determine the ecological conditions of the area where the photovoltaic power station is located, so that managers can make timely response strategies.

[0007] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is: A method for comprehensively evaluating carbon emission reduction in a centralized photovoltaic power station and carbon budget of an ecosystem is provided, which comprises the following steps: S1. Determine the spatial scope of the photovoltaic power station based on remote sensing images, and determine the impact area of ​​the photovoltaic power station with the photovoltaic power station as the center and the impact threshold as the radius; S2. Calculate the net primary productivity of the affected area using a high-resolution net primary productivity estimation method, and construct a monthly / annual productivity time series B for the affected area based on the net primary productivity; S3. Calculate net ecosystem productivity based on net primary productivity and soil respiration consumption ; Input remote sensing attribute variables and climate factors of the impact area into the productivity prediction model to obtain the net ecosystem productivity ; S4. Net ecosystem productivity using weighted average fusion method and Perform integration to generate a productivity integration value; and based on the integration value, calculate the carbon budget of the affected area within a set time range; S5. Obtain the average annual power generation and marginal power emission factor of the photovoltaic power station within the set time range, and calculate the carbon emission reduction based on the average annual power generation and marginal power emission factor within the set time range; S6. Calculate the ratio of carbon balance to the sum of carbon emission reduction and carbon balance as the ecological contribution indicator ECCR. When ECCR is positive, the larger the value, the stronger the carbon sink capacity of the ecosystem. When ECCR is negative, it indicates that the system is a carbon source.

[0008] The beneficial effects of the above technical solution are: this method integrates the net ecosystem productivity and , it can achieve a systematic, dynamic and high-precision assessment of the regional carbon balance of photovoltaic power stations, comprehensively covering the carbon absorption and carbon emission processes, and effectively overcome the problems of traditional methods such as reliance on a single estimation path and poor spatial adaptability. Through the coordinated input of remote sensing attribute variables and climate factors, the productivity prediction model can output carbon balance maps on a monthly or annual scale at high spatial resolution, meeting the assessment needs of multi-regional and multi-type photovoltaic projects. The generated NEP layer and ecological carbon benefit response index (ECCR) can not only be used to evaluate the ecological impact of photovoltaic projects after their completion, but also to build an "environmental characteristics-carbon benefit response" knowledge base through the accumulation of multiple regions and multiple samples, which can feed back into the site selection, design optimization and intelligent operation and maintenance of photovoltaic projects, and has good engineering promotion and practical application value.

[0009] Furthermore, the scheme introduces the ECCR metric, which can be used to quantitatively assess the contribution of the ecosystem's carbon budget to overall carbon benefits. Higher values ​​indicate a stronger regional carbon sequestration capacity and a healthier ecosystem carbon budget. During the operation phase of a photovoltaic power station, regular updates of observed or estimated carbon emission reductions and carbon budgets are used to dynamically monitor changes in the metric. A downward trend in the metric indicates a weakening of the ecosystem's carbon sink function, necessitating timely ecological intervention measures. A negative trend indicates a shift from a carbon sink to a carbon source, deteriorating the ecosystem's carbon budget. If the metric remains persistently low, it is necessary to assess whether overcrowding of modules or microclimate effects is inhibiting vegetation growth, thereby impacting the ecosystem's carbon absorption capacity. Ecological management measures such as replanting, grassland maintenance, drainage optimization, or adjustments to power station operation can be implemented in a timely manner to improve the carbon budget while ensuring a healthy ecosystem.

[0010] Furthermore, the net ecosystem productivity is calculated The expression is: , in, is the net primary productivity; is soil respiration consumption; Tavg is average temperature; prep is precipitation; is an exponential function; Calculating net ecosystem productivity The expression is: Where f is the machine learning model; X is the input feature vector; Net ecosystem productivity and The expression for fusion is: ; ; 0≤ , ≤1; in, is the productivity integration value corresponding to the yth time step, and its time scale is day, month or year; and All are assigned weights; and Respectively and The mean absolute error between the observed values ​​and the validation dataset; The expression for calculating the carbon budget of the impact area during the study period is: Where T is the total number of time steps in the study period.

[0011] Furthermore, the high-resolution net primary productivity estimation method is used to calculate the net primary productivity data of the affected area, including: S21. Determine the spatial extent of the impact of power station construction on the ecosystem based on high-resolution remote sensing images and the boundaries of the photovoltaic power station construction, and define the impact zone; S22. Extract daily NDVI data of the affected area based on high spatial resolution remote sensing images and calculate daily photosynthetically active radiation absorption ratio (FPAR) and leaf area index (LAI); S23. Obtain a daily-scale meteorological dataset for the affected area, and resample the data to the same spatial resolution as the high-resolution remote sensing image based on the same coordinate system; S24. Calculate gross primary productivity (GPP) based on the light energy utilization model according to the resampled daily meteorological data and the daily photosynthetically active radiation absorption ratio (FPAR). S25. Calculate the net primary productivity (NPP) based on the gross primary productivity (GPP), plant respiratory consumption, and leaf area index (LAI).

[0012] The beneficial effect of the above technical solution is that most existing public primary productivity datasets (such as MODIS GPP and FluxSat GPP) have spatial resolutions ranging from hundreds of meters to kilometers, making it difficult to accurately capture local vegetation changes in areas with small-scale centralized photovoltaic power plant construction and complex spatial distribution. As a result, when fine-tuning vegetation growth differences around photovoltaic power plants, existing data often cannot meet the research accuracy requirements, making it difficult to accurately assess the impact of photovoltaic power plants on local vegetation and carbon budgets. This solution uses the above method to accurately capture the dynamic impact of centralized photovoltaic power plant construction on regional vegetation growth and carbon budgets, thereby overcoming the shortcomings of existing technologies and providing data support for the calculation of the ecological contribution indicator ECCR.

[0013] Furthermore, the daily-scale meteorological dataset includes at least precipitation prep, maximum temperature maxtmp, minimum temperature mintmp, average temperature Tavg, saturated water vapor pressure difference VPD, and downward shortwave radiation SWRad.

[0014] Furthermore, the calculation method of daily NDVI data includes: Based on high spatial resolution remote sensing images, NDVI composite images with optimal cloud-free pixels are synthesized based on the original temporal resolution intervals; If there are missing data within a time period, historical trend statistics or adjacent time series interpolation are used to fill the missing data; then smoothing is applied to the NDVI composite image; According to the smoothed sequence, the composite NDVI series was converted into a daily NDVI series by linear interpolation.

[0015] Furthermore, the expressions for calculating the daily scale photosynthetically active radiation absorption ratio and leaf area index value are: in, and are the minimum and maximum values ​​of the daily NDVI data, respectively; and They are The minimum and maximum values ​​of is the maximum value of the leaf area index; The expression of the light energy utilization model is: , , in, is the gross primary productivity; is the light energy utilization efficiency, is the amount of available light energy absorbed; For maximum light energy utilization efficiency; is the temperature stress factor; It is a water stress factor; and for The empirical thresholds used in the calculations correspond to the dryness levels at which plants are completely uninhibited and completely inhibited, respectively; and for The empirical thresholds used in the calculation correspond to the lowest temperature limits when plant photosynthesis is completely inhibited and completely uninhibited, respectively.

[0016] Furthermore, the expression for calculating net primary productivity is: in, is the net primary productivity; The amount of carbon consumed by vegetation leaf respiration; The amount of carbon consumed by plant roots to maintain respiration; the amount of carbon consumed by respiration for vegetation growth; is the specific leaf area; is the basal rate of leaf respiration; is the ratio of root to leaf biomass; is the basal rate of root respiration; is the temperature coefficient of respiration rate.

[0017] Furthermore, the comprehensive assessment method for carbon emission reduction of centralized photovoltaic power stations and ecosystem carbon budget also includes an assessment of the carbon budget impact of the region where the photovoltaic power station is located: A1. The ecological disturbance range of the PV power station is used as the study area, and an area with a similar natural environment as the study area is selected as the control area. The construction start and end time of the PV power station are identified and divided into the pre-construction period and the post-construction period. A2. Calculate primary productivity raster data of the study area and the control area using high-resolution net primary productivity estimation method; A3. Based on the generated productivity raster data, the maximum value synthesis method is used to process the study area and the control area on a monthly or annual scale to generate the productivity time series A of the study area and the control area; A4. Calculate the carbon budget dynamic characteristic indicators of the study area and the control area based on the productivity time series A; A5. Based on the dynamic characteristic indicators of carbon budget, calculate the indicator differences between the study area and the control area, and the indicator differences between the pre-construction period and the post-construction period.

[0018] The beneficial effect of the above technical solution is: when the parameters corresponding to the difference between the above two indicators are both positive, it indicates that the construction of the photovoltaic power station plays a positive role in the local ecological restoration. When selecting sites for other photovoltaic power stations in the future, areas with a natural environment similar to that of the photovoltaic power station can be selected for photovoltaic power station construction; if the parameters corresponding to the difference between the two indicators are both positive and negative, it is necessary to make a comprehensive judgment based on the ECCR indicator. If the ECCR indicator is positive, an area with a natural environment similar to that of the photovoltaic power station can be selected for photovoltaic power station construction. If the ECCR indicator is negative, it indicates that the construction of the photovoltaic power station will damage the local ecological environment. Therefore, when constructing new photovoltaic power stations in the future, areas with a natural environment similar to that of the photovoltaic power station need to be avoided.

[0019] Furthermore, the expression for calculating the dynamic characteristic index of carbon budget is: , in, is the magnitude of change in primary productivity; and is the primary productivity at the i-th and i+1-th time points; m is the time series length of primary productivity; Primary productivity and the time interval between is the i-th time point; and are the values ​​and time averages of primary productivity, respectively; is the average rate of change of primary productivity; For the changing trend; The difference between the study area and the control area is the horizontal analysis index, and its calculation expression is: , , in, and are the magnitude of changes in primary productivity in the study area and the control area, respectively; represents the difference in the magnitude of change in primary productivity between the study area and the control area; and are the average change rates of primary productivity in the study area and the control area, respectively; represents the difference in the average rate of change of primary productivity between the study area and the control area; and are the trends of primary productivity in the study area and the control area, respectively; It represents the difference in the long-term trend of primary productivity between the study area and the control area; The difference between the indicators before and after the construction period is the longitudinal analysis indicator, and its calculation expression is: , , in, and are the magnitude of change in primary productivity during the pre-construction period and the post-construction period, respectively; It represents the difference in the magnitude of change in primary productivity before and after construction; and are the average change rates of primary productivity in the pre-construction period and the post-construction period, respectively; It represents the difference in the average rate of change of primary productivity before and after construction; and These are the trends of primary productivity changes in the pre-construction period and the post-construction period; Represents the difference in long-term trend changes in primary productivity before and after construction.

[0020] The beneficial effects of the above technical solution are: after adopting the above method, this solution can sensitively identify the carbon absorption potential in the early stage of vegetation restoration when NEP has not yet turned positive, avoiding misjudging the restored area as a carbon source area; it can also more comprehensively depict the ecological change trend under the influence of photovoltaics through horizontal (control area) and vertical (time series) two-dimensional analysis.

[0021] Furthermore, the comprehensive assessment method of carbon emission reduction of centralized photovoltaic power stations and carbon balance of ecosystems also includes calculating the total carbon benefit C=a+b of the photovoltaic power station area, where a is the carbon emission reduction and b is the carbon balance.

[0022] The beneficial effects of the above technical solution are: the larger the C value, the more it indicates that the region has achieved significant synergistic gains in carbon emission reduction and ecological carbon balance effects after the construction and operation of the photovoltaic power station, with good ecological and energy dual benefits, and can provide a construction and promotion demonstration for photovoltaic projects in areas with similar ecological conditions.

[0023] This plan establishes a total carbon benefit (C) database by calculating the total carbon benefits of different photovoltaic power stations. In this way, during the photovoltaic power station site selection stage, regions with similar natural and environmental factors such as topography, climate conditions, vegetation types and land use as areas with higher C values ​​are prioritized for photovoltaic power station layout. This reflects the forward-looking estimation of regional carbon benefits and ecological suitability assessment, and improves the scientificity and rationality of the project spatial layout. During the design stage, the C value can provide a decision-making reference for determining the scale of photovoltaic power stations (such as installed capacity and land area), and assist in formulating engineering plans with greater ecological and carbon benefits. During the operation stage, by continuously monitoring the interannual variation trend of the C value, the dynamic changes in the ecological restoration and carbon benefits of photovoltaic power stations in the long-term operation can be judged. If the C value decreases, the vegetation restoration strategy or ecological intervention measures can be adjusted in a timely manner to ensure stable system operation and maximize carbon benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a flow chart of the comprehensive assessment method for carbon emission reduction of centralized photovoltaic power stations and carbon budget of ecosystems.

[0025] Figure 2 Flowchart showing the method for calculating net primary productivity in the impact area using the high-resolution net primary productivity estimation method.

[0026] Figure 3 Flowchart of a method for assessing the carbon budget impact of a region where a photovoltaic power station is located.

[0027] Figure 4 To generate a spatial comparison map of NPP data and MODIS-NPP data; (a) is the generated NPP data submap; (b) is the MODIS NPP data submap.

[0028] Figure 5 To generate a comparison chart of the numerical accuracy of NPP data and MODIS-NPP data.

[0029] Figure 6 It is the time series curve of NPP in the affected area and the control area.

[0030] Figure 7 This is a comparison chart of the NPP time series changes before (t1) and after (t2) the construction of a centralized photovoltaic power station.

[0031] Figure 8 is the time series curve of net ecosystem productivity (NEP). DETAILED DESCRIPTION

[0032] The specific embodiments of the present invention are described below to facilitate understanding of the present invention by those skilled in the art. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations utilizing the concepts of the present invention are protected.

[0033] refer to Figure 1 , Figure 1 The flowchart of the comprehensive assessment method of carbon emission reduction of centralized photovoltaic power station and carbon budget of ecosystem is shown; Figure 1 As shown, the method S includes steps S1 to S6.

[0034] In step S1, the spatial scope of the photovoltaic power station is determined based on remote sensing images, and the impact area of ​​the photovoltaic power station is determined with the photovoltaic power station as the center and the impact threshold as the radius; the impact threshold can be dynamically adjusted according to the regional ecological and environmental characteristics and research objectives.

[0035] In step S2, the net primary productivity of the affected area is calculated using a high-resolution net primary productivity estimation method, and a monthly / annual productivity time series B of the affected area is constructed based on the net primary productivity; The detailed implementation process for constructing the monthly / yearly productivity time series B for the impact area is as follows: A spatial mask is created for the impact area S. At each time point, the net primary productivity values ​​for all grids within the mask are extracted and the regional average is calculated to construct the original time series. Subsequently, a specific time scale (monthly / yearly) is selected and the maximum composite method (MVC) is applied. The maximum value in each month / year of the time series is selected as the output value, resulting in a complete and continuous monthly / yearly net primary productivity time series for the impact area S (i.e., productivity time series B).

[0036] like Figure 2 As shown, in one embodiment of the present invention, the use of a high-resolution net primary productivity estimation method to calculate net primary productivity data in the affected area includes: S21. Determine the spatial extent of the impact of power station construction on the ecosystem and, subsequently, the impact zone based on high-spatial-resolution remote sensing imagery and the boundaries of the PV power station construction. In this plan, "high spatial resolution" refers to satellite data with a resolution of ten meters or even meters, as opposed to common products (such as MODIS and VIIRS) that have a resolution of hundreds of meters or even kilometers. This allows for more precise capture of spatial variations in surface vegetation within the PV power station area.

[0037] S22. Extract daily NDVI data of the affected area based on high spatial resolution remote sensing images. The detailed implementation process of this step is as follows: Based on high-spatial-resolution remote sensing images, NDVI composite images with optimal cloud-free pixels are synthesized based on the original temporal resolution intervals. If there are missing data within a time period, historical trend statistics or adjacent time series interpolation are used to fill the missing data. Then, smoothing is applied to the NDVI composite image. Based on the smoothed sequence, the composite NDVI sequence is converted into a daily-scale NDVI sequence through linear interpolation.

[0038] Then, based on the daily NDVI data, the daily photosynthetically active radiation absorption ratio and leaf area index value are calculated: in, is the daily scale photosynthetically active radiation absorption ratio; is the leaf area index value; and are the minimum and maximum values ​​of the daily NDVI data, respectively; and They are The minimum and maximum values ​​of is the maximum value of the leaf area index; S23. Obtain a daily meteorological dataset for the affected area. The dataset includes at least variables such as precipitation (prep), maximum temperature (maxtmp), minimum temperature (mintmp), average temperature (Tavg), saturation vapor pressure difference (VPD), and downward shortwave radiation (SWRad). Then, resample the data to the same spatial resolution as the high-resolution remote sensing imagery based on the same coordinate system to achieve spatial matching of multi-source data.

[0039] S24. Calculate the gross primary productivity (GPP) based on the light energy utilization efficiency model according to the resampled daily meteorological data and the daily photosynthetically active radiation absorption ratio (FPAR). The expression of the light energy utilization efficiency model is: , , in, is the gross primary productivity; is the light energy utilization efficiency, is the amount of available light energy absorbed; For maximum light energy utilization efficiency; is the temperature stress factor; It is a water stress factor; and for The empirical thresholds used in the calculations correspond to the dryness levels at which plants are completely uninhibited and completely inhibited, respectively; and for The empirical thresholds used in the calculation correspond to the lowest temperature limits when plant photosynthesis is completely inhibited and completely uninhibited, respectively.

[0040] S25. Calculate the net primary productivity (NPP) based on the gross primary productivity (NPP), plant respiratory consumption, and leaf area index (LAI): in, is the net primary productivity; The amount of carbon consumed by vegetation leaf respiration; The amount of carbon consumed by plant roots to maintain respiration; the amount of carbon consumed by respiration for vegetation growth; is the specific leaf area; is the basal rate of leaf respiration; is the ratio of root to leaf biomass; is the basal rate of root respiration; is the temperature coefficient of respiration rate.

[0041] This scheme optimizes the net primary productivity through the above method, which can improve the regional applicability of parameters and the accuracy of model calculation results, and further enhance the practicality and promotion value of this scheme method.

[0042] In step S3, the net ecosystem productivity is calculated based on the net primary productivity and soil respiration consumption. ; Input remote sensing attribute variables and climate factors of the impact area into the productivity prediction model to obtain the net ecosystem productivity .

[0043] When implemented, this option preferably calculates net ecosystem productivity The expression is: , in, is the net primary productivity; is soil respiration consumption; Tavg is average temperature; prep is precipitation; is an exponential function.

[0044] This scheme calculates net ecosystem productivity The expression is: Where f is a machine learning model that has been trained based on existing NEP observation samples (such as FLUXNET flux tower data, field observations at typical sites, etc.) and has been improved through feature importance analysis and hyperparameter optimization; X is the input feature vector, which covers multiple remote sensing attribute variables and climate factors. After model f is trained and verified, the long sequence data of each variable contained in the feature vector X of the impact area is used to predict the long sequence of the impact area. .

[0045] Productivity prediction models can be random forests, gradient boosting trees, support vector regression, or neural networks, enabling the fitting and extrapolation of NEP at different spatial and temporal scales. Input features can include multiple remote sensing attribute variables (such as leaf area index (LAI), photosynthetically active radiation absorption rate (FPAR), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), etc.) and climatic factors (such as temperature, precipitation, and saturation vapor pressure deficit). Feature importance analysis and hyperparameter optimization can be used during feature selection and model construction to improve model performance.

[0046] Given the potential lack of sufficient field observational data in the study area, the model training data comes from existing NEP observation samples (e.g., flux tower FLUXNET and field observations). Model training primarily relies on existing sample point data or publicly available observation datasets (e.g., FLUXNET or other representative regional site observations). These existing datasets are representative in terms of spatial distribution and ecological types, allowing for the construction of a generalizable prediction model. Once trained, the model can be applied to the study area to obtain NEP data for various time periods, providing support for further carbon budget research.

[0047] In step S4, the net ecosystem productivity is calculated using a weighted average fusion method. and Integration to generate stable and reliable productivity integration value; net ecosystem productivity and The expression for fusion is: ; ,0≤ , ≤1; in, is the productivity integration value corresponding to the yth time step, and its time scale can be days, months or years; and All are assigned weights. and Represent the mechanism-driven estimates and data-driven estimates The mean absolute error between the predictions and the observed values ​​on the validation dataset is used to measure the prediction accuracy of the two methods.

[0048] This scenario affects net ecosystem productivity and Fusion can combine the advantages of different methods and calculate The expression has an ecological mechanism basis and strong generalization ability, while the productivity prediction model can flexibly adapt to multi-dimensional inputs and has high accuracy. The fusion strategy helps to make up for the differences between the two in local adaptability and overall stability, while improving the high-resolution spatiotemporal prediction ability and series continuity, and can generate stable and reliable productivity fusion values.

[0049] Then, based on the fusion value, the carbon budget of the affected area within the set time range is calculated: Where T is the total number of time steps in the study period, which depends on the time scale used (such as daily, monthly or annual scale) and the total time span.

[0050] In step S5, the average annual power generation and marginal power emission factor of the photovoltaic power station within the set time range are obtained, and the carbon emission reduction amount is calculated based on the average annual power generation and marginal power emission factor within the set time range; This average annual power generation data can be derived from the following three sources: For established and operational PV power plants, measured power output data can be used; for plants under construction or in the planning stages, power generation capacity can be estimated based on sunlight resource assessments and equipment parameters; and for long-term operational forecast analysis, average power generation values ​​over multiple future years can be derived based on model predictions. By unifying the timescale and measurement methodology, the comparability and applicability of subsequent carbon emission reduction calculations can be ensured.

[0051] The marginal emission factor (EF, kg CO2 / kWh) for electricity is used to calculate the amount of carbon dioxide emitted per kilowatt-hour of electricity. This factor can be obtained from the national or regional grid emission factor and is usually calculated based on the grid's energy structure. For example, the grid emission factor varies across regions in China. The formula for calculating the marginal emission factor for electricity is as follows: in, is the marginal emission factor for electricity; It is the capacity marginal emission factor, and the data of different regional power grids are generally obtained based on the "regional power grid baseline emission factor" released by the country every year. and are the weights of OM and BM, respectively, usually set to 0.75 and 0.25.

[0052] In this scenario, the expression for calculating carbon emission reduction is: Where a is the carbon emission reduction (kg CO2); t is the year index, from the first year to the nth year; is the annual electricity generation in year t (kWh); is the marginal emission factor of electricity in year t (kg CO2 / kWh).

[0053] In step S6, the ratio of the carbon balance to the total carbon benefit (the sum of carbon emissions reductions and carbon balance) is calculated as the ecological contribution indicator (ECCR). A positive ECCR indicates a stronger carbon sink capacity of the ecosystem; a negative ECCR indicates that the system is a carbon source. Ecological managers in the area where the PV power station is located can use the ECCR to determine the ecological status and, when ecological risks are present, promptly implement ecological optimization measures, such as replanting, grassland maintenance, and drainage optimization.

[0054] like Figure 3 As shown in Figure 2, the comprehensive assessment method for carbon emission reduction of centralized photovoltaic power stations and carbon budget of ecosystems in this scheme also includes an assessment of the carbon budget impact of the region where the photovoltaic power station is located: A1. The ecological disturbance range of the PV power station is used as the study area, and an area with a similar natural environment as the study area is selected as the control area. The construction start and end time of the PV power station are identified and divided into the pre-construction period and the post-construction period. A2. Use high-resolution net primary productivity estimation method to calculate primary productivity (PP, including gross primary productivity (GPP) and net primary productivity (NPP)) raster data of the study area and the control area.

[0055] A3. Based on the generated productivity raster data, the maximum value synthesis method is used to process the study area and the control area on a monthly or annual scale to generate the productivity time series A of the study area and the control area. The detailed implementation process of this step is as follows: A spatial mask is created for the impact area S and the control area C. At each time point, the PP values ​​of all grids within the mask are extracted and the regional average is calculated to construct the original time series. Subsequently, a time scale (monthly / yearly) is selected and the maximum value of the monthly / yearly maximum value in the time series is applied using the Maximum Value Composition (MVC) method. This outputs a complete and continuous time series of monthly or yearly data for areas S and C (i.e., productivity time series A).

[0056] A4. Calculate the dynamic characteristic indicators of carbon budget in the study area and the control area based on productivity time series A: , in, is the magnitude of change in primary productivity; and is the primary productivity at the i-th and i+1-th time points; m is the time series length of primary productivity; Primary productivity and the time interval between is the i-th time point; and are the values ​​and time averages of primary productivity, respectively; is the average rate of change of primary productivity; For the changing trend; A5. Based on the dynamic characteristic indicators of carbon budget, calculate the difference between the study area and the control area, and the difference between the pre-construction period and the post-construction period. The difference between the study area and the control area is the horizontal analysis indicator, and its calculation expression is: , , in, and are the changes in primary productivity in the study area and the control area, The difference in the magnitude of primary productivity changes between the study area and the control area can reflect the relative changes in the magnitude of carbon budget fluctuations. and are the average change rates of primary productivity in the study area and the control area, The difference in the average rate of change of primary productivity between the study area and the control area can reflect the difference in the average rate of change of carbon budget per unit time. and are the primary productivity change trends of the study area and the control area, It indicates the difference in the long-term trend change of primary productivity between the study area and the control area, which can reflect the difference in the long-term trend change rate of carbon budget.

[0057] The difference between the indicators before and after the construction period is the longitudinal analysis indicator, and its calculation expression is: , , in, and are the changes in primary productivity before and after construction, It represents the difference in the magnitude of changes in primary productivity before and after construction, which can reflect the relative changes in the fluctuation of carbon budget; and are the average change rates of primary productivity before and after construction, It represents the difference in the average rate of change of primary productivity before and after construction, which can reflect the difference in the average rate of change of carbon budget per unit time; and are the changing trends of primary productivity before and after construction, It represents the difference in the long-term trend change of primary productivity before and after construction, which can reflect the difference in the long-term trend change rate of carbon budget.

[0058] The above parameters 、 and as well as 、 and If the difference is positive, it indicates that the PV plant has carbon sink potential; if it is negative, it may lead to a carbon source effect. A larger difference indicates a more significant impact of the PV plant on the carbon budget. A larger positive difference indicates a stronger carbon sink potential, while a larger negative difference indicates a more significant carbon source effect.

[0059] After extracting the dynamic characteristics of the carbon budget, this solution conducts a comprehensive analysis of the ecological carbon effects of the photovoltaic power station area from both horizontal and vertical dimensions, aiming to identify whether photovoltaic development has caused significant changes in the carbon budget. These parameters can also guide the construction of photovoltaic power stations: When the differences between the indicators corresponding to the horizontal and vertical directions are both positive, it indicates that the construction of the photovoltaic power station has a positive effect on the local ecological restoration. When selecting sites for other photovoltaic power stations in the future, areas with similar natural environments to the photovoltaic power station can be selected for photovoltaic power station construction; if the differences between the indicators corresponding to the horizontal and vertical directions are both positive and negative, it is necessary to make a comprehensive judgment based on the ECCR indicator. If the ECCR indicator is positive, areas with similar natural environments to the photovoltaic power station can be selected for photovoltaic power station construction. If the ECCR indicator is negative, it indicates that the construction of the photovoltaic power station will damage the local ecological environment. Therefore, when constructing new photovoltaic power stations in the future, areas with similar natural environments to the photovoltaic power station need to be avoided.

[0060] During implementation, this plan prefers a comprehensive assessment method for carbon emission reductions of centralized photovoltaic power stations and carbon balance of ecosystems, which also includes calculating the total carbon benefit C=a+b of the photovoltaic power station area, where a is the carbon emission reduction and b is the carbon balance.

[0061] For example: 1. High-resolution net primary productivity estimation method Based on the above method, high-spatial-resolution primary productivity data for a centralized photovoltaic power station were generated. Given the scarcity of daily meteorological datasets for precipitation (prep), maximum temperature (maxtmp), minimum temperature (mintmp), saturation vapor pressure deficit (VPD), and downward shortwave radiation (SWRad), the existing daily-scale public dataset HRLT was selected. This dataset has a spatial resolution of 1 km and covers gridded temperature and precipitation data for China from 1961 to 2019, including prep, maxtmp, and mintmp. VPD and SWRad variables were calculated using the MT-CLIM method based on the daily prep, maxtmp, and mintmp data.

[0062] Landsat imagery was used as remote sensing data. The Normalized Difference Vegetation Index (NDVI) was calculated based on this imagery, and daily NDVI data were obtained. The corresponding daily-scale photosynthetically active radiation absorption ratio (FPAR) and leaf area index (LAI) were calculated. During the parameter assignment process, the following parameter values ​​were determined: NDVI_min = 0.03, NDVI_max = 0.96, FPAR_min = 0.001, and FPAR_max = 0.95. LAI_max represents the maximum potential LAI for each land cover type. Different values ​​were assigned to different land cover types within the region. See Table 1 for specific corresponding values.

[0063] Table 1 BPLUT biogeochemical parameter lookup table for MOD17 algorithm Daily meteorological data were resampled to the same spatial resolution (30 m) and coordinate system as the remote sensing data to achieve spatial matching of the multi-source data. Finally, based on the processed daily meteorological data, LAI, and FPAR data, a light utilization efficiency model was used to calculate gross primary productivity (GPP) and net primary productivity (NPP). Parameter values ​​were assigned according to the MOD17 algorithm parameter table, with different parameter values ​​corresponding to different land cover types (Table 1).

[0064] Finally, the generated primary productivity dataset has the same temporal resolution (16 days) and spatial resolution (30m) as the remote sensing data, and is compared with the MODIS 1km resolution product. The results show that this method can effectively identify details that MODIS cannot capture due to resolution limitations (such as Figure 4 Further accuracy verification shows that the correlation coefficient (R 2 ) reaches 0.71, and the root mean square error (RMSE) is 5.66gC / m². For example Figure 5 This indicates that the high spatial resolution primary productivity data generated by this method has high credibility and can be used for subsequent analysis.

[0065] 2. Evaluate the carbon budget impact of the region where the PV power station is located Taking a typical centralized photovoltaic power station in Gonghe County, Qinghai Province, as an example, this proposal applies methods corresponding to A1-A5 to assess the ecological impact of the PV station on the regional carbon budget. First, based on remote sensing imagery, land use data, and supporting information, the spatial boundaries of the PV station were defined. A 1000-meter buffer zone was set around the station to define the study area (Region S). Furthermore, an undeveloped natural area with similar climate, topography, soil, and vegetation conditions was selected as a control area (Region C) to eliminate interference from environmental background differences. Using tools such as Google Earth and relevant construction data, the study period (2000-2019) was divided into two phases: period t1 (2000-2013), before the construction of the centralized photovoltaic power station, and period t2 (2014-2019), after its construction. In terms of data processing, relying on previously developed high-resolution primary productivity estimation methods, an annual net primary productivity (NPP) dataset for Regions S and C from 2000 to 2019 was constructed. The time series data were processed using the maximum composite value (MVC) method to extract time series data for effective pixels in each region. Figure 6 The NPP time series comparison chart of the impact area S and the control area is shown. Figure 7 The comparison of NPP time series changes in the impact area S before (t1) and after (t2) the construction of the photovoltaic power station is shown. Subsequently, the dynamic characteristic indicators of carbon budget in each region are calculated. The results show that the overall change of the NPP time series curve in the impact area S is greater than that in the control area C ( Figure 6 Table 2 shows the change amplitude and average change rate of the NPP time series curves in the affected area S and the control area C.

[0066] From the changes in indicators in Table 2, it can be seen that the impact area S gC / m², gC / m² / month, while the control area C gC / m², gC / m² / month. This shows that the construction of photovoltaic power stations has increased the volatility of vegetation carbon budget, while the vegetation carbon budget in its natural state is relatively stable. Further horizontal difference analysis, gC / m², = gC / m² / month, which shows that the construction of photovoltaic power stations will promote the improvement of vegetation carbon absorption capacity. Figure 7) also showed that after the power plant construction, the CA, ACR, and k values ​​in region S all increased significantly (by 29.77 gC / m², 0.001 gC / m² / month, and 0.0105, respectively), further verifying the positive ecological impact of the photovoltaic project on improving the regional carbon budget. This example fully demonstrates the feasibility and effectiveness of this method in assessing the impact of centralized photovoltaic power plants on the carbon budget.

[0067] 3. Calculation of Ecological Contribution Index (ECCR) In order to quantify the dynamic changes in carbon budget caused by the construction of photovoltaic power stations on vegetation growth, the ecological contribution index ECCR is calculated according to steps S1 to S6 to conduct a quantitative assessment of vegetation carbon budget based on primary productivity data.

[0068] A centralized photovoltaic power station was selected as the research object. Based on the regional ecological and environmental characteristics and research objectives, the threshold for vegetation impact was determined to be 1000m. With the power station as the center, a buffer area was set based on the vegetation impact threshold range of 1000m, and the impact area S was delineated. Based on the "high-resolution net primary productivity estimation method", a 30m spatial resolution primary productivity (PP) dataset of the impact area S from 2000 to 2019 was generated. Based on the mask of the impact area S, at each time node, the PP values ​​of all grids within the mask range were extracted and the regional average was calculated to obtain the original time series. The maximum value composite method (MVC) was applied to this time series, and the maximum value of PP each year was selected as the output value to construct an annual PP time series dataset of the impact area S. Climate factor data (such as precipitation and temperature) of the impact area S were simultaneously extracted, and a time composite method matching the PP data was used to construct a climate factor time series dataset with the same length as the PP time series. Based on the PP data and climate factor data combined with the vegetation respiratory loss model, the net ecosystem productivity (NEP) was estimated, and the annual-scale NEP time series curve from 2000 to 2019 was obtained ( Figure 8 ).

[0069] from Figure 8 As can be seen, before the construction of the PV power station (2000-2012), NEP values ​​were mostly negative, indicating that the region was primarily a carbon source during this period, with a significant trend in carbon emissions. Since the construction of the PV power station in 2013, NEP values ​​have increased annually and gradually exceeded zero, indicating that the region has gradually transformed into a carbon sink and significantly increased carbon absorption after the PV power station was built. Specifically, in the years following construction, NEP values ​​showed a significant positive trend, reflecting the positive impact of the PV power station on the regional ecosystem's carbon budget, namely, its beneficial effects on ecological restoration, and the region's gradual transformation into a carbon sink with the potential for net carbon absorption.

[0070] The promotion and application value of this plan my country's photovoltaic industry has experienced rapid growth over the past decade, with installed capacity and annual power generation accounting for a continuously expanding share of the power system. This has provided strong impetus for clean energy development and the achievement of the dual carbon goals. Centralized photovoltaic power stations, as the primary form of photovoltaic power station development, offer advantages such as high power generation efficiency, large construction scale, and convenient maintenance and management, demonstrating enormous potential for carbon reduction. However, these power stations are often built in ecologically fragile areas such as deserts and Gobi deserts, potentially disrupting ecological processes such as water, energy, and carbon, impacting regional climate and ecosystem stability. Therefore, in addition to focusing on their carbon reduction benefits, a comprehensive assessment of their impact on the ecosystem's carbon budget is also necessary.

[0071] The present invention comprehensively analyzes the multi-dimensional impact of centralized photovoltaic power stations on regional carbon balance, and proposes a comprehensive carbon balance assessment method based on the combination of remote sensing data and ground observations, and the integration of ecological process simulation and data-driven algorithms. This method not only takes into account the carbon emission reduction benefits achieved by photovoltaic power stations in energy substitution, but also systematically depicts the dynamic changes in carbon sources and carbon sinks caused by them in the ecosystem. Compared with traditional assessment methods, the core innovation of the present invention is reflected in the following four aspects: First, it realizes the coordinated assessment of carbon emission reduction and ecosystem carbon balance, breaking through the limitation of existing methods that only focus on the carbon emission reduction benefits of energy systems, and constructs a "power generation emission reduction-ecological carbon sink" dual-path analysis framework; second, it constructs a high-precision data-driven assessment model, deeply integrates high-resolution remote sensing data with a spatial resolution of 30 meters or even 10 meters with ecosystem process models, and establishes a photovoltaic power station carbon balance database. Compared with traditional kilometer-level products, the spatial accuracy has been improved by two orders of magnitude, significantly enhancing the spatial resolution of ecosystem carbon budget assessment, and providing solid data support for the refined ecological management and optimized layout design of photovoltaic power stations; third, a regional adaptive assessment system is established. This method is applicable to a variety of ecosystem types, such as deserts, grasslands, farmlands, etc. It can construct a "typical ecological response feature library" based on the carbon budget assessment results of existing photovoltaic power stations, and combine regional climate, soil, topography and other multi-factor information to support the scientific planning and site selection of new photovoltaic projects and the prediction of potential ecological risks, and realize "local conditions" photovoltaic ecological layout and management; fourth, the integration of machine learning algorithms and physical mechanism models realizes the unification of the characterization and physical interpretation of complex nonlinear relationships in ecosystem carbon budget calculations, and improves the scientific nature and predictive ability of carbon budget assessments.

[0072] Compared with existing studies that are mostly limited to the quantitative accounting of carbon emission reduction or case analysis of a single ecological zone, the present invention not only systematically covers the carbon emission reduction benefit assessment of photovoltaic power stations, but also comprehensively incorporates the impact on the ecological environment and the potential ecological carbon sink effect during its construction process, realizing a full-cycle, full-factor integrated assessment of carbon effects. At the same time, it has good regional migration capabilities and method versatility, showing significant innovative advantages. This method can be widely used in ecological and environmental impact assessment, carbon accounting, ecological compensation mechanism formulation, and green site selection and planning of national and provincial photovoltaic projects, providing key technical support for the coordinated advancement of clean energy development and ecological protection, and has broad prospects for promotion and application.

Claims

1. A comprehensive assessment method for carbon emission reduction in centralized photovoltaic power stations and carbon budget of ecosystems, characterized by: Including steps: S1. Determine the spatial scope of the photovoltaic power station based on remote sensing images, and determine the impact area of ​​the photovoltaic power station with the photovoltaic power station as the center and the impact threshold as the radius; S2. Calculate the net primary productivity of the affected area using a high-resolution net primary productivity estimation method, and construct a monthly / annual productivity time series B for the affected area based on the net primary productivity; S3. Calculate net ecosystem productivity based on net primary productivity and soil respiration consumption ; Input remote sensing attribute variables and climate factors of the impact area into the productivity prediction model to obtain the net ecosystem productivity ; S4. Net ecosystem productivity using weighted average fusion method and Perform integration to generate a productivity integration value; and based on the integration value, calculate the carbon budget of the affected area within a set time range; S5. Obtain the average annual power generation and marginal power emission factor of the photovoltaic power station within the set time range, and calculate the carbon emission reduction based on the average annual power generation and marginal power emission factor within the set time range; S6. Calculate the ratio of carbon balance to the sum of carbon emission reduction and carbon balance as the ecological contribution indicator ECCR; When ECCR is positive, a larger value indicates that the ecosystem has a stronger carbon sink capacity, while a negative ECCR indicates that the system is a carbon source.

2. The method for comprehensive assessment of carbon emission reduction in centralized photovoltaic power stations and carbon budget of ecosystems according to claim 1, characterized in that: Calculating net ecosystem productivity The expression is: ， in, is the net primary productivity; is soil respiration consumption; Tavg is average temperature; prep is precipitation; is an exponential function; Calculating net ecosystem productivity The expression is: Where f is the machine learning model; X is the input feature vector; Net ecosystem productivity and The expression for fusion is: ； ；0≤ ， ≤1； in, is the productivity integration value corresponding to the yth time step, and its time scale is day, month or year; and All are assigned weights; and Respectively and The mean absolute error between the observed values ​​and the validation dataset; The expression for calculating the carbon budget of the impact area during the study period is: Where T is the total number of time steps in the study period.

3. The method for comprehensive assessment of carbon emission reduction in centralized photovoltaic power stations and carbon budget of ecosystems according to claim 1, characterized in that: The method for calculating the net primary productivity in the impact area using high-resolution net primary productivity estimation methods includes: S21. Determine the spatial extent of the impact of power station construction on the ecosystem and define the impact zone based on high-resolution remote sensing images and the boundaries of the photovoltaic power station construction; S22. Extract daily NDVI data of the affected area based on high spatial resolution remote sensing images and calculate daily photosynthetically active radiation absorption ratio (FPAR) and leaf area index (LAI); S23. Obtain a daily-scale meteorological dataset for the affected area, and resample the data to the same spatial resolution as the high-resolution remote sensing image based on the same coordinate system; S24. Calculate gross primary productivity (GPP) based on the light energy utilization model according to the resampled daily meteorological data and the daily photosynthetically active radiation absorption ratio (FPAR). S25. Calculate the net primary productivity (NPP) based on the gross primary productivity (GPP), plant respiratory consumption, and leaf area index (LAI).

4. The method for comprehensive assessment of carbon emission reduction in centralized photovoltaic power stations and carbon budget of ecosystems according to claim 3 is characterized in that: The daily meteorological dataset includes at least precipitation prep, maximum temperature maxtmp, minimum temperature mintmp, average temperature Tavg, saturated water vapor pressure difference VPD, and downward shortwave radiation SWRad.

5. The method for comprehensive assessment of carbon emission reduction in centralized photovoltaic power stations and carbon budget of ecosystems according to claim 4, characterized in that: The calculation methods of daily NDVI data include: Based on high spatial resolution remote sensing images, NDVI composite images with optimal cloud-free pixels are synthesized based on the original temporal resolution intervals; If there are missing data within a time period, historical trend statistics or adjacent time series interpolation are used to fill the missing data; then smoothing is applied to the NDVI composite image; According to the smoothed sequence, the composite NDVI series was converted into a daily NDVI series by linear interpolation.

6. The method for comprehensive assessment of carbon emission reduction in centralized photovoltaic power stations and carbon budget of ecosystems according to claim 5, characterized in that: The expressions for calculating the daily photosynthetically active radiation absorption ratio and leaf area index value are: in, and are the minimum and maximum values ​​of the daily NDVI data, respectively; and They are The minimum and maximum values ​​of is the maximum value of the leaf area index; The expression of the light energy utilization model is: ， ， in, is the gross primary productivity; is the light energy utilization efficiency, is the amount of available light energy absorbed; For maximum light energy utilization efficiency; is the temperature stress factor; It is a water stress factor; and for The empirical thresholds used in the calculations correspond to the dryness levels at which plants are completely uninhibited and completely inhibited, respectively; and for The empirical thresholds used in the calculation correspond to the lowest temperature limits when plant photosynthesis is completely inhibited and completely uninhibited, respectively.

7. The method for comprehensive assessment of carbon emission reduction in centralized photovoltaic power stations and carbon budget of ecosystems according to claim 6, characterized in that: The expression for calculating net primary productivity is: in, is the net primary productivity; The amount of carbon consumed by vegetation leaf respiration; The amount of carbon consumed by plant roots to maintain respiration; the amount of carbon consumed by respiration for vegetation growth; is the specific leaf area; is the basal rate of leaf respiration; is the ratio of root to leaf biomass; is the basal rate of root respiration; is the temperature coefficient of respiration rate.

8. The method for comprehensive assessment of carbon emission reduction in centralized photovoltaic power stations and carbon budget of ecosystems according to any one of claims 1 to 7, characterized in that: It also includes an assessment of the carbon budget impact of the region where the photovoltaic power station is located: A1. The ecological disturbance range of the PV power station is used as the study area, and an area with a similar natural environment as the study area is selected as the control area. The construction start and end time of the PV power station are identified and divided into the pre-construction period and the post-construction period. A2. Calculate primary productivity raster data of the study area and the control area using high-resolution net primary productivity estimation method; A3. Based on the generated productivity raster data, the maximum value synthesis method is used to process the study area and the control area on a monthly or annual scale to generate the productivity time series A of the study area and the control area; A4. Calculate the carbon budget dynamic characteristic indicators of the study area and the control area based on the productivity time series A; A5. Based on the dynamic characteristic indicators of carbon budget, calculate the indicator differences between the study area and the control area, and the indicator differences between the pre-construction period and the post-construction period.

9. The method for comprehensive assessment of carbon emission reduction in centralized photovoltaic power stations and carbon budget of ecosystems according to claim 7, characterized in that: The expression for calculating the dynamic characteristic index of carbon budget is: ， in, is the magnitude of change in primary productivity; and is the primary productivity at the i-th and i+1-th time points; m is the time series length of primary productivity; Primary productivity and the time interval between is the i-th time point; and are the values ​​and time averages of primary productivity, respectively; is the average rate of change of primary productivity; For the changing trend; The difference between the study area and the control area is the horizontal analysis index, and its calculation expression is: ， ， in, and are the magnitude of changes in primary productivity in the study area and the control area, respectively; represents the difference in the magnitude of change in primary productivity between the study area and the control area; and are the average change rates of primary productivity in the study area and the control area, respectively; represents the difference in the average rate of change of primary productivity between the study area and the control area; and are the trends of primary productivity in the study area and the control area, respectively; It represents the difference in the long-term trend of primary productivity between the study area and the control area; The difference between the indicators before and after the construction period is the longitudinal analysis indicator, and its calculation expression is: ， ， in, and are the magnitude of change in primary productivity during the pre-construction period and the post-construction period, respectively; It represents the difference in the magnitude of change in primary productivity before and after construction; and are the average change rates of primary productivity in the pre-construction period and the post-construction period, respectively; It represents the difference in the average rate of change of primary productivity before and after construction; and These are the trends of primary productivity changes in the pre-construction period and the post-construction period; Represents the difference in long-term trend changes in primary productivity before and after construction.

10. The method for comprehensive assessment of carbon emission reduction in centralized photovoltaic power stations and carbon budget of ecosystems according to claim 1, characterized in that: It also includes calculating the total carbon benefit C=a+b of the photovoltaic power station area, where a is the carbon emission reduction and b is the carbon balance.

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