A Big Data-Based Evaluation Method and System for Urban Low-Carbon and Near-Zero-Carbon Effectiveness

Abstract

This invention discloses a method and system for evaluating the effectiveness of urban low-carbon and near-zero carbon initiatives based on big data. The method includes: obtaining an energy structure cleanliness index by analyzing energy generation data; obtaining a transportation carbon efficiency index by analyzing transportation operation data; calculating an industry-building carbon synergy index by statistically analyzing the correlation data of different industries within the city; obtaining a building carbon sink capacity index and a circular economy carbon loop rate by analyzing urban planning data; and calculating the city's carbon metabolism health by combining the energy structure cleanliness index, transportation carbon efficiency index, and industry-building carbon synergy index. Finally, the method compares the city's carbon metabolism health with a set of effectiveness evaluation thresholds to determine the city's carbon metabolism health status. This addresses the problem that existing technologies cannot comprehensively reflect the overall situation of urban low-carbon and near-zero carbon development, and are prone to bias and limitations.

Description

Technical Field

[0001] This invention relates to the field of urban low-carbon effectiveness evaluation, specifically to a method and system for evaluating urban low-carbon and near-zero carbon effectiveness based on big data. Background Technology

[0002] Cities are major sources of energy consumption and carbon emissions, and their low-carbon and near-zero-carbon development is crucial for achieving global carbon reduction goals. By establishing a scientific and reasonable evaluation method for the effectiveness of urban low-carbon and near-zero-carbon development, we can comprehensively and accurately understand the strengths and weaknesses of cities in the process of low-carbon development, promptly identify existing problems, and take targeted improvement measures. This not only helps cities achieve their own sustainable development goals but also makes a positive contribution to addressing global climate change. At the same time, accurate evaluation results can enhance public awareness and acceptance of the low-carbon development concept, guide residents to adopt low-carbon lifestyles, and promote a positive atmosphere for green development throughout society.

[0003] Existing evaluation methods often focus on data collection in one or a few specific areas, such as energy consumption or traffic emissions, while neglecting the integration of multi-domain and multi-dimensional data involved in urban low-carbon and near-zero carbon development. For example, they fail to fully consider multi-source data such as green energy generation data from power plants, traffic operation data from different modes of transportation within the city, data on the interconnected layers of different industries within the city, and urban planning data. This results in evaluation results that cannot comprehensively reflect the overall situation of urban low-carbon and near-zero carbon development, and are prone to bias and limitations.

[0004] Existing evaluation methods often use overly broad indicators that fail to accurately reflect the key characteristics of urban low-carbon and near-zero carbon development. For example, in the industrial and construction sectors, there is a lack of effective indicators that reflect the low-carbon synergy between industrial development and construction, as well as the carbon sequestration capacity of buildings and the carbon closed loop of the circular economy. This prevents the evaluation results from deeply revealing the internal mechanisms and synergistic effects of urban low-carbon development. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a big data-based method for evaluating the effectiveness of urban low-carbon and near-zero carbon development. This method at least solves the problems of existing technologies, such as the inability to comprehensively reflect the overall situation of urban low-carbon and near-zero carbon development, the tendency to be one-sided and limited, and the fact that most of the evaluation indicators used are too broad and cannot accurately reflect the key characteristics of urban low-carbon and near-zero carbon development.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a big data-based method for evaluating the effectiveness of urban low-carbon and near-zero carbon emissions, comprising:

[0007] Step 1: Establish data acquisition devices within power generation companies to collect real-time energy generation data from various green energy sources, and obtain an energy structure cleanliness index by analyzing the energy generation data. ;

[0008] Step 2: Obtain traffic operation data for different modes of transportation within the urban area; and obtain the traffic carbon efficiency index by analyzing the traffic operation data. ;

[0009] Step 3: Calculate the industrial-construction carbon synergy index by statistically analyzing the correlation data of different industries within the city. ;

[0010] Step 4: Obtain urban planning data and analyze it to obtain the building carbon sequestration capacity index. and the carbon closed-loop rate of the circular economy And combined with the clean energy structure index Transportation carbon efficiency index Carbon Coordination Index of Production and Construction Calculating urban carbon metabolism health ;

[0011] Step 5: Set a set of effectiveness evaluation thresholds to include urban carbon metabolism health. By comparing with a set of effectiveness assessment thresholds, the carbon metabolism health status of a city can be determined.

[0012] In the preferred scheme of the above-mentioned big data-based evaluation method for urban low-carbon and near-zero carbon effectiveness, in step one, collecting and analyzing energy generation data of various green energy sources yields the following results:

[0013] By installing power generation metering devices on the power generation equipment of green energy companies, the power generation of different types of green energy can be collected. ;

[0014] The total power generation is obtained by collecting and summing the electricity data from various metering points in real time through the power grid's automated metering system. ;

[0015] Initial carbon emission factors are obtained from ecological databases. Obtain carbon capture rate from suppliers of carbon capture devices. Obtain the rated conversion efficiency of the energy conversion equipment from the manufacturer. Install real-time monitoring equipment on energy conversion equipment to obtain the actual conversion efficiency. 'i' represents the green energy sequence number; the dynamic carbon emission factor is calculated using the above parameters. The calculation formula used is as follows: ;

[0016] Obtain information at time through the power dispatching system t inner city center r Green energy power generation in each administrative region and time t Total green energy generation in all administrative regions , t Indicates a time period. r Indicates the administrative district number; electricity generated through green energy. and total green energy power generation Calculate the proportion of green energy power generation to total green energy power generation. The calculation formula is: ;pass Calculate the spatiotemporal distribution entropy of green energy The calculation formula is: .

[0017] In the preferred scheme of the above-mentioned big data-based evaluation method for urban low-carbon and near-zero carbon effectiveness, the green energy power generation is used as an example. Total power generation Dynamic carbon emission factors and the spatiotemporal distribution entropy of green energy Analysis was conducted to obtain the energy structure cleanliness index. The calculation formula used is as follows:

[0018] ,

[0019] in, This represents the amount of electricity generated by the i-th type of green energy. This represents the total amount of electricity generated. denoted as the static carbon emission factor of the i-th type of green energy.

[0020] In the preferred scheme of the above-mentioned big data-based evaluation method for urban low-carbon and near-zero carbon effectiveness, in step two, acquiring and analyzing traffic operation data of different modes of transportation within the urban area yields the following results:

[0021] The number of passengers using various modes of transportation is obtained over a period of time by traffic flow monitoring equipment, and then the proportion of passengers using each mode of transportation to the total number of passengers is calculated. ;

[0022] By analyzing multiple travel samples for each mode of transportation using data from smart transportation cards, mobile phone signaling, or traffic surveys, the average travel distance for each mode is calculated and used as the average travel distance for different modes of transportation. ;

[0023] By collecting data on the types and consumption of energy used by different modes of transportation, and combining this data with corresponding carbon emission coefficients, the carbon emissions per passenger kilometer for each mode of transportation are calculated. ;

[0024] The average passenger volume of different modes of transportation is statistically analyzed, and the load factor of each mode of transportation is further calculated. , =Average passenger capacity of each mode of transportation / Maximum passenger capacity;

[0025] For each mode of transportation, the ratio of its carbon emissions per unit distance on congested road sections to its carbon emissions per unit distance on uncongested road sections is calculated and used as the congestion impact coefficient. .

[0026] In the optimal scheme of the above-mentioned big data-based evaluation method for urban low-carbon and near-zero carbon effectiveness, the proportion of people traveling by various modes of transportation to the total number of travelers is considered. Average travel distance Carbon emissions per passenger kilometer Full load rate weight and congestion impact coefficient Calculating the traffic carbon efficiency index The formula used is as follows:

[0027] ;

[0028] in, Indicates the carbon efficiency index for transportation; Indicates the first l The proportion of people traveling by this mode of transportation to the total number of travelers; Indicates the first l Average travel distance for each mode of transportation; Indicates the first l Carbon emissions per passenger kilometer for each mode of transportation; Indicates the first l The weighting of the occupancy rate of each mode of transportation; Indicates the first l The congestion impact coefficient of each mode of transportation. l Indicates the sequence number of the mode of transportation.

[0029] In the preferred scheme of the above-mentioned big data-based evaluation method for urban low-carbon and near-zero carbon effectiveness, in step three, by statistically analyzing the correlation data of different industries within the city, we can obtain:

[0030] Obtain the number of buildings with installed photovoltaics in the industry. The total number of buildings in industry g Calculate building photovoltaic penetration rate The formula used is: ; This represents the building-integrated photovoltaic (BIPV) penetration rate of the g-th industry; g represents the sequence number of the different industries.

[0031] Obtain the number of logistics vehicles in industry g. Total building area of ​​industry g Calculate logistics vehicle density The formula used is: , This represents the density of logistics vehicles in the g-th industry.

[0032] Collect energy consumption data from different industries to obtain the total carbon emissions of each industry. ;

[0033] Statistics on total energy consumption and total output value of different industries are compiled to calculate energy consumption intensity. The formula used is: Based on total industrial carbon emissions; Calculate carbon emission service efficiency The formula used is: ;

[0034] By statistically analyzing the total building area of ​​all industries and total number of equipment Calculate the total number of units in stock. The formula used is: .

[0035] In the preferred scheme of the above-mentioned big data-based evaluation method for urban low-carbon and near-zero carbon effectiveness, the building photovoltaic penetration rate is used as an example. Logistics vehicle density Total industrial carbon emissions Energy consumption intensity Carbon emission service efficiency The proportion of new energy equipment Total number of vehicles in operation Calculate the carbon synergy index of production and construction The formula used is:

[0036] ;

[0037] in, This represents the building-integrated photovoltaic (BIPV) penetration rate of the g-th industry. This represents the density of logistics vehicles in the g-th industry. This represents the total carbon emissions of the g-th industry. This represents the energy consumption intensity of the g-th industry; This represents the carbon emission service efficiency of the g-th industry; This indicates the proportion of new energy equipment in the g-th industry.

[0038] In the preferred scheme of the above-mentioned big data-based evaluation method for urban low-carbon and near-zero carbon effectiveness, in step four, the different vegetation types in the city and the vegetation area of ​​each type are statistically analyzed. and growth period And obtain the carbon sequestration rate per unit area of ​​different vegetation types. Calculate the amount of carbon sequestration by vegetation The formula used is: ;

[0039] The amount of rainwater reused (HY) is obtained through the rainwater harvesting system, and the carbon emission intensity (TF) of tap water production is obtained through water management data. The carbon emission reduction (JP) caused by rainwater reuse replacing tap water is calculated using the following formula: ;

[0040] Carbon sequestration by vegetation Rainwater carbon emission reduction JP and total building area Calculating the building carbon sequestration capacity index The formula used is: ,in, Indicates the climate coefficient;

[0041] By monitoring energy consumption during the production of recycled resources Combining different energy carbon emission factors The carbon emission reduction NY from recycled resources compared to primary resources is calculated using the following formula: ;in, This represents the amount of energy consumed by type z in the primary resource production process of type m. This represents the consumption of the z-th energy source in the process of producing the m-th type of resource from recycled resources; denoted by , m represents the carbon emission factor of the z-th energy source; m represents the index of the renewable resource, and z represents the index of the energy consumed.

[0042] Based on carbon emission reductions from resource recovery (NY) and total industrial carbon emissions. Obtain the carbon closed-loop rate of the circular economy The calculation formula is: .

[0043] In the preferred scheme of the above-mentioned big data-based evaluation method for urban low-carbon and near-zero carbon effectiveness, based on the energy structure cleanliness index... Transportation carbon efficiency index Carbon Coordination Index of Production and Construction Building carbon sequestration capacity index and the carbon closed-loop rate of the circular economy Calculating urban carbon metabolism health The formula used is:

[0044] ;

[0045] in, This represents the actual value of the k-th assessment stage, where k represents the sequence number of the assessment stage in the low-carbon city evaluation system, with values ​​ranging from 1 to 5, corresponding to the energy structure stage, industrial carbon emission stage, transportation carbon emission stage, building carbon sink stage, and circular economy stage, respectively. This represents the baseline value for the k-th evaluation stage;

[0046] The set of effectiveness evaluation thresholds includes effectiveness evaluation threshold one and effectiveness evaluation threshold two;

[0047] When the city's carbon metabolism health When the effectiveness assessment threshold is less than 1, the carbon metabolism health status is judged as poor; when the effectiveness assessment threshold is less than or equal to the city's carbon metabolism health status... When the performance evaluation threshold is less than or equal to the second threshold, the carbon metabolism health status is judged to be moderate; when the performance evaluation threshold is less than or equal to the city's carbon metabolism health status... The carbon metabolism health status is judged to be excellent.

[0048] Beneficial effects:

[0049] This invention provides a method for evaluating the effectiveness of urban low-carbon and near-zero carbon initiatives based on big data, which has the following beneficial effects:

[0050] (1) By monitoring green energy power generation data in real time, key information such as the change in the proportion of green energy in the overall energy structure, power generation stability and sustainability can be dynamically grasped. For example, it can be clearly understood whether the power generation of renewable energy sources such as solar and wind power is on a steady upward trend, as well as the speed and extent of the replacement of traditional energy by green energy, so as to scientifically assess the effectiveness of the city's energy structure in moving towards a cleaner direction.

[0051] (2) In the transportation sector, acquiring traffic operation data of different modes of transportation within an urban area and calculating the traffic carbon efficiency index can provide an in-depth analysis of the carbon emission status of the urban transportation system. It can clarify the relationship between the usage frequency and operating efficiency of various transportation vehicles (such as fuel vehicles, electric vehicles, public transportation, etc.) and their corresponding carbon emissions, thereby assessing the extent to which urban transportation contributes to low carbon emissions by optimizing travel structure and improving transportation efficiency.

[0052] (3) By statistically analyzing the correlation data of different industries within the city, the carbon sequestration index of industry and construction is calculated to fully explore the potential for collaborative carbon reduction among industries. This can clearly show the overall picture of the interrelationships, resource utilization, and carbon emission transmission among various industries during the production process. By obtaining urban planning data and analyzing it to derive the building carbon sequestration capacity index, the enormous potential of urban buildings in carbon absorption and fixation is highlighted.

[0053] (4) By comprehensively integrating the energy structure cleanliness index, transportation carbon efficiency index, industrial-construction carbon synergy index, building carbon sink capacity index, and circular economy carbon closed-loop rate, the city's carbon metabolism health is calculated and compared with the set of effectiveness assessment thresholds, achieving a comprehensive and systematic assessment of the city's low-carbon and near-zero carbon achievements. This technical point breaks through the limitations of previous single-domain evaluations, comprehensively considering the dynamic balance and health status of urban carbon metabolism from multiple dimensions such as energy production, transportation, industrial development, building planning, and circular economy. It can not only accurately identify the city's strengths and weaknesses in the low-carbon development process, but also provide a scientific basis for the city to formulate a comprehensive, coordinated, and targeted low-carbon development strategy, prompting all sectors of the city to work together to steadily move towards the near-zero carbon goal. Attached Figure Description

[0054] Figure 1 This is a schematic diagram illustrating the steps of the big data-based urban low-carbon and near-zero carbon effectiveness evaluation method of the present invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0056] Example 1:

[0057] Please see Figure 1 This invention provides a method for evaluating the effectiveness of urban low-carbon and near-zero carbon initiatives based on big data, including:

[0058] Step 1: Establish data acquisition devices within power generation companies to collect real-time energy generation data of various green energy sources, and obtain an energy structure cleanliness index by analyzing the energy generation data.

[0059] It should be noted that in the process of calculating the clean energy structure index, all parameters involved in each calculation step need to be normalized and preprocessed to eliminate the dimensions of different parameters, so as to facilitate subsequent formula calculations.

[0060] Step 101: Install power generation metering devices on the power generation equipment of energy companies using green energy sources such as solar power plants, wind power plants, hydropower plants, geothermal power plants, and biomass power plants to collect the power generation of different types of green energy. By installing dedicated metering devices on the power generation equipment of different green energy power plants, real-time and accurate data collection of the power generation of each type of green energy has been achieved. This provides accurate basic data for the subsequent calculation of the energy structure cleanliness index, helps to clearly understand the power generation status of various types of green energy, and thus clarifies the position and development trend of green energy in urban energy supply.

[0061] Step 102: Collect electricity data from each metering point in real time through the power grid's automated metering system, sum the data, and obtain the total power generation. By utilizing the power grid's automated system to comprehensively and in real-time collect and summarize electricity generation information from various points within the city, the total electricity generation of the city over a specific period can be determined. This provides crucial data support for determining the proportion of green energy generation in the overall energy supply, helps to understand the city's energy supply structure from a macro perspective, assesses the contribution rate of green energy to the city's total energy supply, and thus provides a basis for energy planning and decision-making.

[0062] Step 103: Obtain initial carbon emission factors through professional ecological databases, such as the EFDB of the Ministry of Ecology and Environment of China and the EFDB of the UK Department for Environment, Science and Technology. Obtain carbon capture rate from suppliers of carbon capture devices. Obtain the rated conversion efficiency of the energy conversion equipment from the manufacturer. Install real-time monitoring equipment on energy conversion equipment to obtain the actual conversion efficiency. The dynamic carbon emission factor is calculated using the above parameters. The calculation formula used is as follows: ;in, Represents the initial carbon emission factor of the i-th green energy source; This represents the carbon capture rate of the i-th green energy source. This represents the rated conversion efficiency of the energy conversion device for the i-th type of green energy. This represents the actual conversion efficiency of the energy conversion equipment for the i-th type of green energy, where i represents the index of the green energy source and takes a positive integer value. Taking into account multiple factors such as the initial carbon emission factor, carbon capture rate, and the rated and actual conversion efficiency of the energy conversion equipment, a dynamic carbon emission factor is calculated using a formula. This more accurately reflects the carbon emission level of each green energy source during actual power generation, taking into account the impact of changes in carbon capture technology and energy conversion efficiency on carbon emissions, thus improving the accuracy and scientific rigor of green energy carbon emission assessment.

[0063] Step 104: Obtain the time through the power dispatching system t inner city center r Green energy power generation in each administrative region and time t Total green energy generation in all administrative regions , t Indicates a time period. r Indicates the administrative district number; electricity generated through green energy. and total green energy power generation Calculate the proportion of green energy power generation to total green energy power generation. The calculation formula is: ;pass Calculate the spatiotemporal distribution entropy of green energy The calculation formula is: By collecting green energy generation data from various administrative regions at different times using a power dispatching system, the proportion of green energy generation in each region and time period is calculated. Then, the spatiotemporal distribution entropy is calculated using the entropy method. A higher entropy value indicates a more balanced distribution of green energy generation across different areas and times within the city, while a lower entropy value indicates a more concentrated distribution. This helps to understand the distribution characteristics of green energy and provides a reference for rationally planning the layout of green energy and optimizing energy resource allocation.

[0064] Step 105: By analyzing green energy power generation Total power generation Dynamic carbon emission factors and the spatiotemporal distribution entropy of green energy Analysis was conducted to obtain the energy structure cleanliness index. The calculation formula used is as follows:

[0065] ,

[0066] in, This represents the amount of electricity generated by the i-th type of green energy. This represents the total amount of electricity generated. denoted as the static carbon emission factor of the i-th type of green energy.

[0067] It should be noted that, Calculate the proportion of electricity generated from the i-th type of green energy in the total electricity generation, reflecting the relative importance of green energy in the energy structure. The higher the proportion, the greater the contribution of green energy to the overall energy supply, initially reflecting the trend towards a cleaner energy structure; This represents the carbon emission intensity during the production or use of the energy source, and is a correction for the actual cleanliness of the green energy. For example, if a certain green energy source... Lower (lower carbon emissions), A larger value indicates a more significant contribution of this energy source to clean energy conversion; conversely, if... If the energy source is relatively less clean (higher emission level), its contribution will be appropriately reduced. This step avoids the one-sidedness of assessing clean energy solely based on its share of electricity generation, and deeply integrates the carbon emission characteristics of energy. Used to measure the evenness of green energy distribution over time (e.g., different time periods) and space (e.g., different regions). The smaller the value, the more even the distribution (e.g., solar and wind energy are more evenly distributed in time and space, reducing the reliance on and fluctuations of traditional energy sources). By amplifying the distribution balance and then taking the logarithm to base 2, the impact of distribution balance on the energy structure cleanliness index becomes more reasonable. For example, the more balanced the distribution, the better. The larger the value, the better. The more obvious the improvement effect, the more important the stability of energy supply is to clean energy. The above calculation results of n kinds of green energy are summed, and the contribution of all green energy is taken into account to obtain a comprehensive energy structure clean energy index.

[0068] Traditional energy structure assessments may only focus on the proportion of green energy generation, neglecting the actual carbon emission intensity of different green energy sources. For example, while some biomass power generation is considered green energy, its production process has high carbon emissions; failing to differentiate between them would overestimate its cleanliness. This solution addresses this issue by... This issue is addressed by adjusting the carbon emissions for each type of green energy source, making the assessment more consistent with actual carbon emissions. Uneven spatial and temporal distribution of green energy (e.g., solar power doesn't generate electricity at night, wind power is greatly affected by seasonal fluctuations) increases energy supply volatility, necessitates reliance on traditional energy sources for peak shaving, and impacts overall clean energy efficiency. Traditional assessments have neglected this factor, while this solution… The quantitative analysis of the impact of spatiotemporal distribution balance addresses the lack of clean energy assessment in relation to energy supply stability, and more comprehensively reflects the actual clean energy level of the energy structure.

[0069] The plan encourages cities to focus not only on the proportion of green energy when optimizing their energy structure, but also on reducing carbon emission intensity (by choosing cleaner green energy technologies) and improving the spatial and temporal balance of energy distribution (such as supporting energy storage facilities and diversifying green energy types). For example, cities will prioritize the development of green energy with low carbon emission factors (such as high-quality wind power and photovoltaics) in their planning, and improve their spatial and temporal distribution balance through energy storage or smart grid technologies to enhance... To achieve a more efficient low-carbon transformation.

[0070] Step 2: Obtain traffic operation data for different modes of transportation within the urban area, and obtain the traffic carbon efficiency index by analyzing the traffic operation data.

[0071] It should be noted that in the process of calculating the traffic carbon efficiency index, all parameters involved in each calculation step need to be normalized and preprocessed to eliminate the dimensions of different parameters, so as to facilitate subsequent formula calculations.

[0072] Step 201: Obtain the number of passengers using various modes of transportation over a period of time through statistics from the city's transportation department or through traffic flow monitoring equipment, and then calculate the proportion of passengers using each mode of transportation to the total number of passengers. By utilizing transportation department statistics and traffic monitoring equipment, it is possible to accurately obtain information on the number of travelers using various modes of transportation, and then calculate their proportion of the total number of travelers. This provides crucial data for subsequent assessments of the carbon emission contributions of each mode of transportation, and helps to understand urban residents' travel preferences and transportation structure.

[0073] Step 202: Analyze multiple travel samples for each mode of transportation using smart card data, mobile phone signaling data, or traffic survey data to calculate the average travel distance for each mode of transportation, which will be used as the average travel distance for different modes of transportation. By leveraging multiple data sources to obtain a large number of travel samples and calculating the average travel distance for each mode of transportation, the travel characteristics of each mode of transportation can be accurately reflected. Travel distance is closely related to energy consumption and carbon emissions; this data helps to accurately calculate the carbon emissions of different modes of transportation throughout the entire travel chain, providing support for optimizing traffic organization and promoting low-carbon travel.

[0074] Step 203: By collecting data on the types and consumption of energy used by different modes of transportation, and combining this data with the corresponding carbon emission coefficients, calculate the carbon emissions per passenger kilometer for each mode of transportation. For example, the amount of gasoline consumed by a gasoline-powered vehicle × the carbon emission coefficient of gasoline / passenger kilometers = the carbon emission per passenger kilometer of the gasoline-powered vehicle. By collecting detailed data on energy types and consumption, and combining this with the corresponding carbon emission coefficients, the carbon emission per passenger kilometer of each mode of transportation can be accurately calculated. This makes carbon emission calculations more scientific and targeted.

[0075] Step 204: The actual passenger volume of public transportation such as buses and subways can be obtained through onboard passenger counting equipment or operational data provided by transportation operators; the actual passenger volume of other modes of transportation, such as private cars, can be calculated through traffic surveys or sampling statistics to calculate the average passenger volume of different modes of transportation, and further calculate the load factor weight of different modes of transportation. , =Average passenger volume of each mode of transportation / Maximum passenger volume. By acquiring actual passenger volume data for public transportation and other modes of transportation through various methods, the average passenger volume and load factor are calculated, providing a clear picture of the passenger capacity and energy efficiency of each mode of transportation. Modes of transportation with high load factors typically have better energy efficiency and lower per capita carbon emissions. This data provides a reference for optimizing transportation operations and improving energy efficiency.

[0076] Step 205: Install traffic flow monitoring equipment, such as induction coils and video surveillance equipment, on major urban roads and traffic nodes to collect vehicle speed and flow data in real time. Simultaneously, utilize road condition information provided by map software to obtain road congestion levels. Integrate the collected traffic flow and speed data with the road condition information from the map software to analyze the speed distribution of different modes of transportation on different road sections and at different times. For each mode of transportation, calculate its average speed and energy consumption on congested and uncongested road sections. Based on the average speed, corresponding energy consumption, and corresponding carbon emission factors of each mode of transportation on congested and uncongested road sections, establish a speed-carbon emission relationship model to determine the carbon emissions per unit distance at different speeds. For each mode of transportation, calculate the ratio of its carbon emissions per unit distance on congested road sections to its carbon emissions per unit distance on uncongested road sections, using this as a congestion impact coefficient. By collecting data in real time through monitoring equipment and combining it with map traffic information, it is possible to accurately grasp the speed distribution and energy consumption of different modes of transportation on congested and uncongested road sections. After establishing a speed-carbon emission relationship model, the carbon emissions per unit distance at different speeds can be determined, and the congestion impact coefficient can be calculated. This helps to comprehensively assess the negative impact of traffic congestion on carbon emissions.

[0077] Step 206: The proportion of people traveling by various modes of transportation to the total number of travelers. Average travel distance Carbon emissions per passenger kilometer Full load rate weight and congestion impact coefficient Calculating the traffic carbon efficiency index The formula used is as follows:

[0078] ;

[0079] in, Indicates the carbon efficiency index for transportation; Indicates the first l The proportion of people traveling by this mode of transportation to the total number of travelers; Indicates the first l Average travel distance for each mode of transportation; Indicates the first lCarbon emissions per passenger kilometer for each mode of transportation; Indicates the first l The weighting of the occupancy rate of each mode of transportation; Indicates the first l The congestion impact coefficient of each mode of transportation. l The number representing the mode of transportation is a positive integer.

[0080] It should be noted that the molecular part middle, Represents the total number of trips (number of people × distance) for this mode of transportation; divided by This reflects the efficiency of the transportation mode in terms of carbon emissions, which is the reciprocal of the carbon emissions per unit of travel. The larger the value, the more efficient the transportation mode is in terms of carbon emissions. This measure is used to assess the impact of occupancy rates on carbon efficiency in actual transportation operations. The higher the occupancy rate, the greater the weight and the more significant the improvement in carbon efficiency. These coefficients reflect the impact of traffic congestion on carbon efficiency; the higher the congestion level, the smaller the coefficient, and the greater the reduction in carbon efficiency. These two coefficients are used to adjust the theoretical carbon efficiency of transportation modes for real-world operational scenarios. The total carbon efficiency contribution of the urban transportation system under the combined influence of various factors is obtained by summing the corrected carbon efficiencies of all modes of transportation. (The denominator is not included in this text.) The total number of trips (number of people × distance) of all modes of transportation in a city is used as a normalization benchmark to eliminate the influence of the total number of trips on carbon efficiency assessment, so that the transportation carbon efficiency of cities of different sizes is comparable. By the ratio of the numerator to the denominator, a relative value is obtained, namely the transportation carbon efficiency index. This index comprehensively reflects the carbon efficiency level of the urban transportation system under the interplay of multiple factors such as trip structure, trip distance, carbon emission intensity, load factor and congestion.

[0081] Traditional transportation carbon assessments often focus on total carbon emissions or the carbon intensity of a single mode of transportation, neglecting the combined impact of factors such as travel structure (e.g., the proportion of trips made by public transport versus private transport), travel distance distribution, operational efficiency (occupancy rate), and traffic conditions (congestion). For example, knowing only that a certain type of vehicle has high carbon emissions per unit mileage without considering its low actual proportion of trips may misjudge its impact on overall transportation carbon efficiency. Previous assessments have not fully considered the occupancy rate and congestion conditions of transportation modes in actual operation. For instance, while buses theoretically have low carbon emissions per passenger, their actual carbon efficiency will decrease if they are frequently empty (low occupancy rate) or if energy consumption increases due to congestion (low congestion impact coefficient). This solution introduces... and This approach addresses the disconnect between theoretical assessments and actual operational scenarios, making the evaluation more realistic. Assessing transportation carbon efficiency solely based on absolute carbon emissions or trip volume fails to provide effective comparisons between cities of different sizes. For example, large cities may have high total trip volume and carbon emissions, but their carbon efficiency may not be low. This solution addresses this issue by using a denominator... Normalize the molecules to eliminate differences in city size, so that It has become a universally applicable relative indicator, solving the problem of difficulty in comparing the carbon efficiency of transportation in cities of different sizes.

[0082] Taking into account multiple factors can more accurately reflect the low-carbon level of an urban transportation system. For example, if a city... A higher proportion indicates a reasonable transportation structure (e.g., a high proportion of public transportation trips), optimized travel distance distribution, low carbon emission intensity of transportation modes, and high operational efficiency (high occupancy rate, low congestion), resulting in significant overall low-carbon transportation achievements; conversely, if... If the level is low, specific influencing factors can be identified (such as an excessively high proportion of private car travel, severe congestion of a certain mode of transportation, etc.), providing a clear direction for low-carbon transformation. Through This index allows policymakers to clearly understand the contribution or drag of different modes of transportation on carbon efficiency. For example, it reveals that a high proportion of private car travel leads to... To address the low carbon emissions, targeted optimization of transportation planning (such as improving public transportation networks and establishing dedicated lanes) and the implementation of policies (such as restricting private car travel and encouraging public transportation) can guide the shift towards a low-carbon travel structure, effectively reducing carbon emissions in the transportation sector. This can be achieved through continuous calculations based on big data. It can dynamically monitor the changing trends of urban transportation carbon efficiency. For example, with the promotion of new energy buses and the use of intelligent transportation systems to alleviate congestion, It should be improved gradually. If it does not meet expectations, problems in policy implementation or technology application should be investigated in a timely manner.

[0083] Step 3: Calculate the production-construction carbon synergy index by statistically analyzing the correlation data of different industries within the city.

[0084] It should be noted that in the process of calculating the production-construction-carbon synergy index, all parameters involved in each calculation step need to be normalized and preprocessed to eliminate the dimensions of different parameters, so as to facilitate subsequent formula calculations.

[0085] Step 301: Obtain the number of buildings with installed photovoltaic systems through IoT platforms such as AWS IoT and Azure IoT. The total number of buildings in industry g By the number of buildings and total number of buildings Calculate building photovoltaic penetration rate The formula used is: ; This represents the building-integrated photovoltaic (BIPV) penetration rate of the g-th industry. This represents the number of buildings with photovoltaic installations in the g-th industry. This represents the total number of buildings with photovoltaic (PV) installations in industry type g, where g represents the industry index and is a positive integer. By leveraging an IoT platform, relevant data can be accurately acquired to calculate the building PV penetration rate, directly reflecting the degree of adoption of PV building integration within an industry. A higher indicator indicates better performance in utilizing solar energy resources and reducing building energy consumption, providing a crucial basis for assessing the green transformation of industries.

[0086] Step 302: Obtain the number of logistics vehicles in industry g through traffic management department registration data or logistics company operation ledgers. Total building area of ​​industry g By the number of logistics vehicles and total building area Calculate logistics vehicle density The formula used is: , This represents the logistics vehicle density of industry type g. Calculated using data from relevant departments and enterprises, the logistics vehicle density reflects the intensity of logistics transportation per unit area of ​​industrial buildings. High logistics vehicle density may indicate frequent transportation activities and higher energy consumption and carbon emissions. This indicator helps identify industries with frequent logistics activities and provides data support for optimizing logistics distribution and promoting new energy logistics vehicles.

[0087] Step 303: Compile energy consumption data for different industries to obtain the total carbon emissions of each industry. By systematically collecting and statistically analyzing energy consumption data from various industries, and combining this data with relevant carbon emission factors, the total carbon emissions of each industry can be accurately calculated. This provides a reliable data foundation for accurately assessing the role of each industry in urban carbon emissions and for formulating targeted emission reduction targets and measures.

[0088] Step 304: Calculate energy consumption intensity by statistically analyzing energy consumption data and industry output data for different industries. The formula used is: ; This represents the energy consumption intensity of industry g; and is based on the total carbon emissions of the industry. Calculate carbon emission service efficiency The formula used is: ; This represents the carbon emission service efficiency of industry g. Energy consumption intensity reflects the amount of energy consumed by an industry to generate one unit of economic output; a lower value indicates higher energy efficiency. Carbon emission service efficiency reflects the amount of carbon emissions generated per unit of economic output; a lower value indicates better carbon emission benefits for the industry. These two indicators comprehensively assess the economic and environmental performance of an industry, helping to guide industries towards high efficiency and low carbon emissions, and promoting the optimization and upgrading of industrial structure.

[0089] Step 305: Statistically analyze the number of new energy devices in different industries. With total number of equipment Calculate the proportion of new energy equipment The formula used is: ;in, This indicates the proportion of new energy equipment in the g-th industry. This represents the number of new energy devices in industry type g. By statistically analyzing the number of new energy devices in the industry and the total number of devices, the proportion of new energy devices is calculated. A higher proportion indicates that the industry is more inclined to use clean energy in its energy structure, which is beneficial for reducing carbon emissions.

[0090] Step 306: Calculate the total building area of ​​all industries. and total number of equipment Calculate the total number of units in stock. The formula used is: By summing the total building area and total equipment quantity of all industries, the total inventory is obtained, which comprehensively reflects the overall scale of urban industries in terms of buildings and equipment. This helps to grasp the material basis of industrial development from a macro perspective and analyze the relationship between industry scale and carbon emissions.

[0091] Step 307: Through building photovoltaic penetration rate Logistics vehicle density Total industrial carbon emissions Energy consumption intensity Carbon emission service efficiency The proportion of new energy equipment Total number of vehicles in operation Calculate the carbon synergy index of production and construction The formula used is:

[0092] ;

[0093] in, Indicates the carbon synergy index between production and construction; This represents the building-integrated photovoltaic (BIPV) penetration rate of the g-th industry. This represents the density of logistics vehicles in the g-th industry. This represents the total carbon emissions of the g-th industry. This represents the energy consumption intensity of the g-th industry; This represents the carbon emission service efficiency of the g-th industry; This indicates the proportion of new energy equipment in the g-th industry.

[0094] It should be noted that, This reflects the extent to which industries utilize building-integrated photovoltaic (BIPV) energy. Reflecting the intensity of industrial logistics activities, This represents the correlation between industry's energy acquisition through building-integrated photovoltaics and logistics vehicle activities, expressed as the natural logarithm. This is a non-linear amplification of the correlation, highlighting the synergistic effect between the two. For example, if an industry has a high building photovoltaic penetration rate (high utilization of clean energy) and a low density of logistics vehicles (relatively low carbon emissions from logistics activities), then... The value is relatively large, and the summation has a positive effect on the carbon synergy index of production and construction, emphasizing the low-carbon synergy between industries in energy acquisition and logistics activities. and This reflects the scale and intensity of industrial carbon emissions. It reflects the relationship between industrial carbon emissions and the efficiency of output services. To comprehensively measure the "pressure" of industrial carbon emissions, the square root is used. The summation is then performed; a larger value indicates greater overall carbon emission pressure on the industry, which has a suppressive effect on the industry-building carbon synergy index. For example, an industry with large total carbon emissions, high energy consumption intensity, and low carbon emission service efficiency... A large value will lower the denominator after summing, thereby reducing the carbon synergy index of production and construction, and prompting industries to pay attention to carbon emission control. This reflects the extent to which the industry applies new energy equipment. As a benchmark, Taking the square root of the proportion of new energy equipment avoids excessive influence of the proportion on the index while still reflecting its role in promoting the synergy between production, construction, and carbon emissions. For example, if an industry has a high proportion of new energy equipment... A large value, when multiplied by the results of the numerator and denominator calculations, can effectively improve the carbon synergy index of production and construction, encouraging industries to increase the application of new energy equipment.

[0095] Traditional assessments may focus only on a single dimension, such as industrial carbon emissions or building energy use, neglecting the synergy between industry and buildings across multiple dimensions, including energy, logistics, and equipment application. For example, looking only at the total industrial carbon emissions without considering whether it utilizes building-integrated photovoltaics (BIPV), the intensity of logistics activities, and the application of new energy equipment fails to comprehensively assess the carbon synergy between industry and buildings. This solution, through multi-parameter fusion, solves the problem of a single assessment dimension, comprehensively reflecting the complex relationship of carbon synergy between industry and buildings. Previous assessments have not paid attention to the correlation between industrial logistics activities and building energy use. For example, if an industry has high logistics vehicle density but low BIPV penetration, traditional assessments may not have considered the impact of this correlation on carbon synergy. This solution, through... Quantifying the correlation between the two addresses the issue of neglecting key factors in the relationship between industry and construction, making the assessment more aligned with actual collaborative scenarios. Traditional methods may not comprehensively consider the industrial carbon emission pressure (total amount, intensity, efficiency) and the application of new energy equipment. For example, if an industry has a high proportion of new energy equipment but faces significant carbon emission pressure, traditional assessments may struggle to accurately determine its carbon synergy effect. This solution addresses this by using a denominator... Reflecting carbon emission pressure, the product part By emphasizing the application of new energy equipment, the problem of not taking comprehensive consideration has been solved, and a scientific assessment of the synergy between production, construction, and carbon has been achieved.

[0096] Through multi-dimensional parameter fusion It can accurately assess the degree of carbon synergy between industry and construction. For example, two industries may have similar total carbon emissions, but one has a high penetration rate of building-integrated photovoltaics, a low density of logistics vehicles, and a high proportion of new energy equipment, while the other is the opposite. Using this formula, the former's carbon emissions can be calculated to be significantly higher. The results will be significantly higher than the latter, clearly distinguishing the advantages and disadvantages of carbon synergy between production and construction, and providing precise basis for urban industrial planning and building energy utilization. The plan encourages cities to focus on increasing building-integrated photovoltaic (BIPV) penetration, optimizing logistics vehicle density, reducing total carbon emissions and intensity, improving carbon emission service efficiency, and increasing the application of new energy equipment in industrial development and building planning. For example, in industrial park planning, cities will prioritize the introduction of building-integrated photovoltaic (BIPV) facilities, rationally control logistics vehicle density, select low-carbon industries and equip them with new energy equipment to improve... To achieve coordinated low-carbon development of production and construction, effectively reduce regional carbon emissions, and unify... The calculation method makes the degree of carbon sequestration in production and construction comparable across different cities. City managers can compare the levels of carbon sequestration in advanced cities. We should learn from successful experiences and draw on their components (such as building photovoltaic penetration rate, proportion of new energy equipment, etc.) to optimize local carbon production and construction synergy models in a targeted manner, promote exchanges and cooperation on low-carbon development among cities, and jointly improve near-zero carbon effectiveness.

[0097] Step 4: Obtain urban planning data and analyze it to obtain the building carbon sequestration capacity index. and the carbon closed-loop rate of the circular economy ; and combined with the clean energy structure index Transportation carbon efficiency index Carbon Coordination Index of Production and Construction Calculating urban carbon metabolism health .

[0098] It should be noted that in calculating the building carbon sequestration capacity index... Circular economy carbon closed-loop rate and urban carbon metabolism health During the process, all parameters involved in each calculation step need to be normalized and preprocessed to eliminate the dimensions of different parameters, so as to facilitate subsequent formula calculations.

[0099] Step 401: Using urban building planning data, statistically analyze the different vegetation types within the city and the vegetation area of ​​each type. and growth period The carbon sequestration rate per unit area of ​​different vegetation types was obtained through standard specifications such as "DB3415T 61-2023 Ecological Carbon Sequestration Accounting Specification". Combined with the growth years of the vegetation vegetation area and carbon fixation rate per unit area Calculate the amount of carbon sequestration by vegetation The formula used is: ; This represents the vegetation area of ​​the f-th type of vegetation. This represents the carbon sequestration rate per unit area of ​​the f-th vegetation. This indicates the number of years the f-th type of vegetation has been planted. This represents the carbon sequestration rate per unit area of ​​the f-th vegetation. By comprehensively considering factors such as vegetation area, growth age, and carbon sequestration rate per unit area, the carbon sequestration capacity of urban vegetation can be accurately calculated, providing a scientific basis for assessing urban carbon sequestration capacity.

[0100] Step 402: Install flow sensors at nodes of the rainwater harvesting system (such as collection tanks and reuse pipes) to monitor the amount of rainwater reused (HY) in real time. Obtain the carbon emission intensity (TF) of tap water production (e.g., Y kgCO2 produced per cubic meter of tap water) through publicly available data from the city's water authority or environmental protection bureau, such as the water company's annual report. Calculate the carbon emission reduction (JP) caused by rainwater reuse replacing tap water, using the following formula: By monitoring rainwater reuse in real time and combining it with the carbon emission intensity of tap water production, the carbon emission reduction generated by rainwater reuse replacing tap water can be calculated intuitively, providing data support for the sustainable management of urban water resources.

[0101] Step 403: Carbon sequestration by vegetation Rainwater carbon emission reduction JP and total building area Calculating the building carbon sequestration capacity index The formula used is: ,in, This represents the climate coefficient; its value ranges from [0.5, 1.1], and is adjusted according to different climate conditions. For example, if the city belongs to the temperate climate zone, the value is 0.8-1.0. By combining the ecological benefits of vegetation carbon sequestration and rainwater emission reduction with the total building area, and taking into account the influence of climate factors, a building carbon sequestration capacity index is formed, providing an important reference for the low-carbon design and planning of urban buildings.

[0102] Step 404: Install energy monitoring equipment and material balance devices on the production line for recycled resources (such as metals, plastics, etc.) to monitor energy consumption (such as electricity and fuel consumption) during the production process of recycled resources. Combining different energy carbon emission factors The carbon emission reduction NY from recycled resources compared to primary resources is calculated using the following formula: ;in, This represents the amount of energy consumed by type z in the primary resource production process of type m. This represents the consumption of the z-th energy source in the process of producing the m-th type of resource from recycled resources; This represents the carbon emission factor of the z-th energy source; by accurately monitoring the energy consumption of renewable resource production and comparing it with the energy consumption of primary resource production, the carbon emission reduction of renewable resource production can be calculated, which helps encourage enterprises to increase the utilization of renewable resources and promote the development of a resource circular economy.

[0103] Step 405: Based on resource recovery carbon emission reductions (NY) and total industrial carbon emissions Obtain the carbon closed-loop rate of the circular economy The calculation formula is: It intuitively reflects the contribution of recycling and utilization of renewable resources to the reduction of urban carbon emissions, and provides a scientific basis for formulating circular economy policies and goals.

[0104] Step 406: Based on the energy structure cleanliness index Transportation carbon efficiency index Carbon Coordination Index of Production and Construction Building carbon sequestration capacity index and the carbon closed-loop rate of the circular economy Calculating urban carbon metabolism health The formula used is:

[0105] ;

[0106] in, This represents the actual value of the k-th assessment stage, where k represents the sequence number of the assessment stage in the low-carbon city evaluation system, with values ​​ranging from 1 to 5, corresponding to the energy structure stage, industrial carbon emission stage, transportation carbon emission stage, building carbon sink stage, and circular economy stage, respectively. This represents the baseline value for the k-th evaluation stage.

[0107] It should be noted that the molecular part It reflects the degree of cleanliness of the energy structure; the higher the value, the more effective the clean measures such as the use of renewable energy are. It reflects the carbon efficiency of the transportation system; the higher the value, the higher the carbon utilization efficiency of transportation. This measures the degree of synergy between the industrial and construction sectors in terms of carbon emissions; a higher value indicates a stronger synergistic effect between the two sectors in carbon emission reduction. The carbon sink capacity of a building is represented by measures such as vegetation carbon sequestration and rainwater harvesting. The higher the value, the greater the building’s carbon sink contribution. This section showcases the contribution of waste resource recycling to carbon emission reduction under a circular economy model; higher values ​​indicate more significant carbon emission reduction effectiveness. Multiplying these indices comprehensively reflects the positive impact of multiple key aspects of a city's carbon metabolism health, including energy, transportation, industry-building synergy, building carbon sinks, and the circular economy. Higher values ​​indicate a more significant synergistic effect in promoting low-carbon development across these aspects. The denominator quantifies the deviation between the city's actual performance and the benchmark level by calculating the sum of the ratios of the absolute values ​​of the differences between the actual and benchmark values ​​for each aspect. A larger denominator indicates a greater gap between the actual and benchmark values, potentially leading to a greater negative impact on the city's carbon metabolism health. The ratio of the numerator to the denominator yields the city's carbon metabolism health score. The numerator comprehensively reflects the positive contribution of each aspect to low-carbon development, while the denominator reflects the reduction effect of the gap between the actual and benchmark values ​​on carbon metabolism health. The combination of these two factors forms a comprehensive quantitative assessment of the city's carbon metabolism health; higher values ​​indicate healthier carbon metabolism and better near-zero carbon performance.

[0108] Traditional urban low-carbon assessments often focus on a single aspect (such as energy structure or transportation carbon emissions), failing to comprehensively reflect the overall state of urban carbon metabolism. This solution integrates indicators from multiple aspects, including energy, transportation, industry-building synergy, building carbon sinks, and the circular economy, to comprehensively assess the health of urban carbon metabolism, thus addressing the limitations of single-dimensional assessments. For example, even if a city has a high degree of clean energy structure, low transportation carbon efficiency and insufficient industry-building carbon synergy may lead to an overestimation of its low-carbon achievements in a single-dimensional assessment. Furthermore, some previous assessments did not adequately consider the comparison between the actual performance of each aspect and reasonable benchmarks, making it difficult to accurately determine the quality of urban low-carbon development.

[0109] By integrating key indicators from multiple stages, this solution can comprehensively and accurately assess the carbon metabolism health of a city. For example, for a city with a high energy structure cleanliness index, a good transportation carbon efficiency index, an outstanding industrial-construction carbon synergy index, strong building carbon sequestration capacity, and a high circular economy carbon closed-loop rate, its... A higher value indicates a healthy carbon metabolism in the city and significant progress towards near-zero carbon emissions; conversely, a lower value indicates a significant weakness in any link (such as a low carbon closure rate in the circular economy). Numerical values ​​clearly reflect the health of a city's carbon metabolism, providing a comprehensive and objective assessment basis for the city's low-carbon development. By calculating the various indices in the numerator and the denominator of the formula, the city's strengths and weaknesses at each stage can be clearly identified. For example, a certain index in the numerator (such as...) The denominator is relatively low, allowing for targeted analysis of problems in the transportation sector (such as low public transport share and high carbon emission intensity of private cars); a specific link in the denominator... A large value indicates a significant gap between the current stage and the benchmark (e.g., the actual value of industrial carbon emissions far exceeds the benchmark value), thus providing a clear direction for improvement in urban low-carbon development. This helps cities optimize each stage in a targeted manner and improve the overall health of carbon metabolism. The design of multiplying the exponents in the numerator of the formula emphasizes the synergistic effect of various urban stages in carbon metabolism, guiding city managers and relevant departments to focus on the coordinated linkage of multiple stages such as energy, transportation, industry-building, building carbon sinks, and the circular economy when promoting low-carbon development.

[0110] It should be noted that, This represents the actual value of each stage of the energy structure, specifically the proportion of clean energy consumption. Data such as urban energy statistical yearbooks and "Energy Consumption Structure Reports" can be obtained through energy big data platforms (e.g., the State Energy Group data system) and calculated. The calculation formula is: .

[0111] This represents the actual value of industrial carbon emissions, specifically the industrial carbon emission intensity per unit of GDP. It can be obtained through carbon accounting platforms (such as carbon footprint, EcoVadis) or enterprise carbon emission direct reporting systems, including enterprise carbon emission monitoring data and industrial added value data from city statistical yearbooks, and then calculated. The calculation formula is: .

[0112] This represents the actual value of carbon emissions from the transportation sector, specifically per capita transportation carbon emissions; transportation carbon emission models (such as the LEAP model), urban transportation big data centers, etc., acquire fuel consumption data, new energy vehicle charging statistics, and Gaode / Baidu Maps travel volume data, and calculate... The calculation formula is: .

[0113] Represents the actual value of the transportation carbon emission link, specifically the building carbon sink capacity; the green building certification data, urban green space remote sensing images (Landsat), and forestry carbon sink monitoring station data can be obtained through GIS software (such as ArcGIS) and carbon sink measurement models (such as InVEST), and calculate , and the calculation formula is: .

[0114] Represents the actual value of the circular economy link, specifically the solid waste resource utilization rate; the annual report on urban solid waste treatment and the data of the solid waste recycling enterprises filed by the environmental protection department can be obtained through the circular economy monitoring platform (such as the system of the Recycling Resources Recycling Association), and calculate , and the calculation formula is: .

[0115] It should be noted that Through the government open data platform (such as the "National Data" of the National Bureau of Statistics) or the standardized database (such as the Global Carbon Atlas of the Global Carbon Database), the "Low-carbon Pilot City Assessment Report" released by the Ministry of Ecology and Environment can be obtained, or the IPCC "National Greenhouse Gas Inventory Guidelines", the benchmark values of the C40 Cities Climate Leadership Alliance, etc. By calculating the average value of low-carbon pilot cities as the benchmark value of the corresponding link.

[0116] Step Five: Set the set of effectiveness evaluation thresholds, and compare the urban carbon metabolism health degree with the set of effectiveness evaluation thresholds to judge the status of the urban carbon metabolism health degree.

[0117] Step 501: The set of effectiveness evaluation thresholds includes the first effectiveness evaluation threshold and the second effectiveness evaluation threshold; when the urban carbon metabolism health degree < the first effectiveness evaluation threshold, it is judged that the carbon metabolism health degree is poor, indicating that the carbon metabolism problem is prominent, and the synergy of links such as energy, transportation, and industry is poor, and a comprehensive low-carbon transformation needs to be promoted; when the first effectiveness evaluation threshold ≤ the urban carbon metabolism health degree < the second effectiveness evaluation threshold, it is judged that the carbon metabolism health degree is medium, indicating that the carbon metabolism health degree is average, there are obvious short boards, and low-carbon measures need to be strengthened; when the second effectiveness evaluation threshold ≤ the urban carbon metabolism health degree , it is judged that the carbon metabolism health degree is excellent, the synergy effect of each link is significant, the energy structure is clean, the transportation carbon efficiency, the production and construction synergy, the building carbon sink, and the circular economy are excellent, the carbon emission is in an efficient control state, and it is close to the zero-carbon goal.

[0118] It should be noted that the effectiveness assessment threshold can be obtained by acquiring historical data of the city and calculating the historical urban carbon metabolism health at different historical points in time, such as the past 5 years. And calculate the average value, standard deviation, etc., based on the historical carbon metabolism health of a city, such as the past 5 years of a city With a mean of 0.6 and a standard deviation of 0.1, we can initially set the effectiveness evaluation threshold one at 0.6 (mean) and the effectiveness evaluation threshold two at 0.7 (mean + standard deviation), and then adjust them in conjunction with the city's near-zero carbon target. Alternatively, we can select cities with similar economic scale, industrial structure, and geographical characteristics (such as cities that are mainly industrial) and statistically analyze their... The second threshold for performance evaluation is the average or quantile (e.g., the 75th quantile). The first threshold is the difference between the second threshold and the standard deviation of the city's carbon metabolism health. Alternatively, time series models (e.g., ARIMA) or machine learning models can be used, inputting data on the city's future energy transition plan, industrial upgrading plan, and low-carbon transportation measures, to predict... Analyze trends and set reasonable thresholds.

[0119] Example 2:

[0120] A big data-based evaluation system for the effectiveness of urban low-carbon and near-zero carbon initiatives is used to implement the aforementioned big data-based evaluation method for urban low-carbon and near-zero carbon initiatives.

[0121] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0122] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0123] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A big data-based evaluation method for the effectiveness of urban low-carbon and near-zero carbon initiatives, characterized in that: include: Step 1: Establish data acquisition devices within power generation companies to collect real-time energy generation data from various green energy sources, and obtain an energy structure cleanliness index by analyzing the energy generation data. ; Step 2: Obtain traffic operation data for different modes of transportation within the urban area; and obtain the traffic carbon efficiency index by analyzing the traffic operation data. ; Step 3: Calculate the industrial-construction carbon synergy index by statistically analyzing the correlation data of different industries within the city. ;In particular, by statistically analyzing the correlation data of different industries within the city, we can obtain: Obtain the number of buildings with installed photovoltaics in the industry. The total number of buildings in industry g Calculate building photovoltaic penetration rate The formula used is: ; Indicates the first Building photovoltaic penetration rate in various industries; Indicates the serial number of different industries; Obtain the number of logistics vehicles in industry g. Total building area of ​​industry g Calculate logistics vehicle density The formula used is: , Indicates the first The density of logistics vehicles in various industries; Collect energy consumption data from different industries to obtain the total carbon emissions of each industry. ; Statistics on total energy consumption and total output value of different industries are compiled to calculate energy consumption intensity. The formula used is: Based on total industrial carbon emissions; Calculate carbon emission service efficiency The formula used is: ; By statistically analyzing the number of new energy devices in different industries With total number of equipment Calculate the proportion of new energy equipment The formula used is: ;in, Indicates the first The proportion of new energy equipment in various industries Indicates the first The number of new energy equipment in various industries; By statistically analyzing the total building area of ​​all industries and total number of equipment Calculate the total number of units in stock. The formula used is: ; Through building photovoltaic penetration rate Logistics vehicle density Total industrial carbon emissions Energy consumption intensity Carbon emission service efficiency The proportion of new energy equipment Total number of vehicles in operation Calculate the carbon synergy index of production and construction The formula used is: ； in, This represents the building-integrated photovoltaic (BIPV) penetration rate of the g-th industry. This represents the density of logistics vehicles in the g-th industry. This represents the total carbon emissions of the g-th industry. This represents the energy consumption intensity of the g-th industry; This represents the carbon emission service efficiency of the g-th industry; Indicates the first The proportion of new energy equipment in the seed industry; Step 4: Obtain urban planning data and analyze it to obtain the building carbon sequestration capacity index. and the carbon closed-loop rate of the circular economy And combined with the clean energy structure index Transportation carbon efficiency index Carbon Coordination Index of Production and Construction Calculating urban carbon metabolism health Among them, according to the clean energy structure index Transportation carbon efficiency index Carbon Coordination Index of Production and Construction Building carbon sequestration capacity index and the carbon closed-loop rate of the circular economy Calculating urban carbon metabolism health The formula used is: ； in, This represents the actual value of the k-th evaluation stage, where k represents the sequence number of the evaluation stage in the low-carbon city evaluation system, with a value ranging from 1 to 5. This represents the actual value of the energy structure, specifically the proportion of clean energy consumption. This represents the actual value of industrial carbon emissions, specifically the industrial carbon emission intensity per unit of GDP. This represents the actual value of carbon emissions from transportation, specifically per capita carbon emissions from transportation. This represents the actual value of carbon emissions from the transportation sector, specifically the carbon sequestration capacity of buildings. This represents the actual value of the circular economy process, specifically the solid waste resource utilization rate. Indicates correspondence Benchmark values ​​for the evaluation process; Step 5: Set a set of effectiveness evaluation thresholds to include urban carbon metabolism health. By comparing with a set of effectiveness assessment thresholds, the carbon metabolism health status of a city can be determined.

2. The method for evaluating the effectiveness of urban low-carbon and near-zero carbon emissions based on big data as described in claim 1, characterized in that, In step one, collecting and analyzing energy generation data from various green energy sources yields the following results: By installing power generation metering devices on the power generation equipment of green energy companies, the power generation of different types of green energy can be collected. ; The total power generation is obtained by collecting and summing the electricity data from various metering points in real time through the power grid's automated metering system. ; Initial carbon emission factors are obtained from ecological databases. Obtain carbon capture rate from suppliers of carbon capture devices. Obtain the rated conversion efficiency of the energy conversion equipment from the manufacturer. Install real-time monitoring equipment on energy conversion equipment to obtain the actual conversion efficiency. 'i' represents the green energy sequence number; the dynamic carbon emission factor is calculated using the above parameters. The calculation formula used is as follows: ; The green energy generation of the r-th administrative district in the city within time t is obtained through the power dispatch system. Total green energy generation of all administrative regions within time t t represents the time period, r represents the administrative region number; through green energy power generation and total green energy power generation Calculate the proportion of green energy power generation to total green energy power generation. The calculation formula is: ;pass Calculate the spatiotemporal distribution entropy of green energy The calculation formula is: .

3. The method for evaluating the effectiveness of urban low-carbon and near-zero carbon emissions based on big data as described in claim 2, characterized in that, By analyzing green energy power generation Total power generation Dynamic carbon emission factors and the spatiotemporal distribution entropy of green energy Analysis was conducted to obtain the energy structure cleanliness index. The calculation formula used is as follows: ； in, This represents the amount of electricity generated by the i-th type of green energy. This represents the total amount of electricity generated. denoted as the static carbon emission factor of the i-th type of green energy.

4. The method for evaluating the effectiveness of urban low-carbon and near-zero carbon emissions based on big data as described in claim 3, characterized in that, In step two, traffic operation data for different modes of transportation within the urban area are acquired and analyzed to obtain the following results: The number of passengers using various modes of transportation is obtained over a period of time by traffic flow monitoring equipment, and then the proportion of passengers using each mode of transportation to the total number of passengers is calculated. ; By analyzing multiple travel samples for each mode of transportation using data from smart transportation cards, mobile phone signaling, or traffic surveys, the average travel distance for each mode is calculated and used as the average travel distance for different modes of transportation. ; By collecting data on the types and consumption of energy used by different modes of transportation, and combining this data with corresponding carbon emission coefficients, the carbon emissions per passenger kilometer for each mode of transportation are calculated. ; The average passenger volume of different modes of transportation is statistically analyzed, and the load factor of each mode of transportation is further calculated. , ; For each mode of transportation, the ratio of its carbon emissions per unit distance on congested road sections to its carbon emissions per unit distance on uncongested road sections is calculated and used as the congestion impact coefficient. .

5. The method for evaluating the effectiveness of urban low-carbon and near-zero carbon emissions based on big data as described in claim 4, characterized in that, The proportion of people traveling by various modes of transportation to the total number of travelers Average travel distance Carbon emissions per passenger kilometer Full load rate weight and congestion impact coefficient Calculating the traffic carbon efficiency index The formula used is as follows: ； in, Indicates the carbon efficiency index for transportation; This represents the proportion of travelers using the l-th mode of transportation out of the total number of travelers. This represents the average travel distance for the l-th mode of transportation; This represents the carbon emissions per passenger kilometer for the l-th mode of transportation. This represents the load factor weight of the l-th mode of transportation. This represents the congestion impact coefficient of the l-th mode of transportation, where l represents the sequence number of the mode of transportation.

6. The method for evaluating the effectiveness of urban low-carbon and near-zero carbon emissions based on big data as described in claim 5, characterized in that, In step four, the different vegetation types within the city and the vegetation area of ​​each type are statistically analyzed. and growth period And obtain the carbon sequestration rate per unit area of ​​different vegetation types. Calculate the amount of carbon sequestration by vegetation The formula used is: ; Rainwater reuse is achieved through rainwater harvesting systems. Carbon emission intensity of tap water production can be obtained through water data. Calculate the carbon emission reduction from rainwater reuse that replaces tap water. The formula is: Calculate the carbon emission reduction from rainwater reuse that replaces tap water. The formula is: ; Carbon sequestration by vegetation Rainwater carbon emission reduction and total building area Calculating the building carbon sequestration capacity index The formula used is: ,in, Indicates the climate coefficient; By monitoring energy consumption during the production of recycled resources Combining different energy carbon emission factors Calculate the carbon emission reduction from resource recycling compared to the production of primary resources. The formula is: ;in, This represents the amount of energy consumed by type z in the primary resource production process of type m. The consumption of the z-th energy source in the process of recycling the m-th resource; denoted by , m represents the carbon emission factor of the z-th energy source; m represents the index of the renewable resource, and z represents the index of the energy consumed. Carbon emission reduction based on resource recycling Total carbon emissions from industries Obtain the carbon closed-loop rate of the circular economy The calculation formula is: .

7. The method for evaluating the effectiveness of urban low-carbon and near-zero carbon emissions based on big data as described in claim 6, characterized in that, The set of effectiveness evaluation thresholds includes effectiveness evaluation threshold one and effectiveness evaluation threshold two; When the city's carbon metabolism health When the effectiveness assessment threshold is set at one, the individual is judged to have poor carbon metabolism health. When the effectiveness evaluation threshold is less than or equal to the city's carbon metabolism health level When the performance evaluation threshold is less than or equal to the second threshold, the carbon metabolism health status is judged to be moderate; when the performance evaluation threshold is less than or equal to the city's carbon metabolism health status... The carbon metabolism health status is judged to be excellent.

8. A big data-based evaluation system for the effectiveness of urban low-carbon and near-zero carbon initiatives, characterized in that: A big data-based evaluation method for achieving the effectiveness of urban low-carbon and near-zero carbon emissions according to any one of claims 1-7.

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