Urban sustainable development evaluation simulation system and method

Abstract

The invention relates to the technical field of strategy evaluation simulation systems, in particular to an urban sustainable development evaluation simulation system and method, and the system comprises a strategy module, an industrial module, a social economy module, an energy module, a water resource module, an agriculture module, an ecological environment module, a land module and a region welfare climate scene module. The module is used for predicting changes of different social ecosystem elements in the future and providing quantitative support for sustainable development. According to the invention, a meta-model system is established, integration of multiple modules and multiple methods is realized, and a complete and advanced evaluation system and integration framework are formed. According to the invention, the elements of each module and the mutual relation among the modules can comprehensively consider multiple aspects of urban development, and comprehensively consider the welfare levels of three dimensions of economy, society and environment, thereby reflecting the comprehensiveness of sustainable development, and improving the development efficiency through the positive cycle of welfare-investment-growth. And interaction and mutual promotion among elements of sustainable development are emphasized, and the accuracy and reliability of evaluation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of evaluating simulation systems, and specifically to an urban sustainable development evaluation simulation system and method. Background Art

[0002] Generally, traditional urban sustainable development evaluation methods usually include the following steps: collecting relevant statistical data and historical materials from various departments and institutions, selecting a series of key indicators according to the evaluation objectives to form an evaluation system, cleaning, sorting, and analyzing the collected data, calculating the values of each indicator, writing an evaluation report based on the analysis results, and putting forward improvement suggestions. Therefore, traditional evaluations often rely on historical data, are difficult to reflect the latest dynamics of urban development in a timely manner, lack dynamic simulation and prediction capabilities, are difficult to evaluate the impact of different strategies or decisions on future sustainable development, and the various modules or indicators often exist in isolation, lacking systematic and comprehensive considerations, and are difficult to flexibly meet the specific needs and scenarios of different cities or regions.

[0003] Although the Department of Economic and Social Affairs, the United Nations Environment Programme, and UNESCO have recently begun to attempt to simulate sustainable development scenarios at the global or national level through dynamic models. However, such comprehensive evaluation systems and methods are only applicable to large-scale simulations, are comprehensive evaluation model frameworks at the global or national level, and cannot insight into the details of urban development, and cannot provide more meaningful guidance for urban sustainable development. Urban sustainable development evaluation is for small scales, integrating more theoretical details, and comprehensively evaluating the long-term development capabilities in the three dimensions of urban economy, society, and environment based on urban analysis. It aims to measure the ability of a city to meet the current development needs without compromising the ability of future generations to meet their needs. The content of urban sustainable development evaluation is extensive, including but not limited to population dynamics, financial status, socioeconomic growth, energy consumption and efficiency, industrial development, agricultural production, ecological environment quality, water resource management, land use planning, and regional well-being levels.

[0004] In summary, there is a need to propose an urban sustainable development evaluation simulation system and method to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to provide an urban sustainable development evaluation simulation system and method to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] Urban Sustainable Development Assessment and Simulation System, which constructs a meta-model system and method based on cutting-edge theories, expert knowledge, and the participation of multiple stakeholders to achieve module integration, including a strategy module, a finance module, a socio-economic module, an energy module, an industry module, an agriculture module, an ecological environment module, a water resources module, a land module, and a regional well-being evaluation module;

[0008] The strategy module is used to provide relevant basic data reflected by population strategies, financial strategies, economic strategies, energy strategies, land management strategies, investment strategies, agricultural strategies, and climate scenarios, such as basic data on population size, population structure, industrial investment, energy utilization structure, and land use type, so as to achieve the simulation and prediction of different modules. The finance module is used to manage the revenue, expenditure, budget, and deficit of local finance, ensure the effective allocation of resources and economic stability, and simulate the impact of different financial decisions on economic development;

[0009] The socio-economic module is used to analyze the driving forces of socio-economic growth and simulate the impact of different economic decisions on regional development;

[0010] The energy module is used to predict regional energy demand, consumption, and carbon dioxide emissions, and simulate the impact of different energy decisions on the environment and the economy;

[0011] The industry module is used to analyze the impact of major industries on regional development and simulate the promoting effect of different industrial decisions on the regional economy;

[0012] The agriculture module is used to predict regional agricultural production;

[0013] The ecological environment module is used to evaluate the impact of different industrial activities on the environment;

[0014] The water resources module is used to analyze the impact of rainfall and evapotranspiration factors on water production;

[0015] The land module is used to predict future land use changes to assist in the planning and management of land resources;

[0016] The regional well-being evaluation module is used to evaluate the impact of different decisions on regional well-being.

[0017] Preferably, the population module considers factors such as fertility rate, mortality rate, and aging during implementation, and simulates population dynamics through the system dynamics method.

[0018] Preferably, the driving forces analyzed by the socio-economic module include capital accumulation, technological progress, and labor supply factors.

[0019] Preferably, the impacts analyzed by the industry module include aspects such as output value, employment, and investment.

[0020] Preferably, the agricultural module is also used to evaluate the impacts of labor, capital, and technological production factors on agricultural output.

[0021] Preferably, the impacts evaluated by the ecological environment module include solid waste, air pollution, and carbon emissions.

[0022] Preferably, the water resources module is also used to simulate the effects of different water resources decisions on regional water resources management.

[0023] Preferably, the land module is also used to simulate the impacts of different land decisions on regional development.

[0024] Preferably, the impacts evaluated by the regional well-being evaluation module include income, wealth distribution, living standards, and health and well-being.

[0025] Based on the above system, the present invention also proposes a comprehensive evaluation and simulation method for urban sustainable development, including the following steps:

[0026] S1. Determine the simulation area and time period, and through the analysis of the strategy module, collect and input the basic data required by other modules, such as historical population data, financial revenue and expenditure records, socio-economic indicators, energy consumption data, industrial output value, agricultural output, ecological environment conditions, water resources situation, land use status, and regional well-being related indicators;

[0027] S2. Considering factors such as fertility rate, mortality rate, and aging, use the system dynamics method to simulate the changes in future population size and structure, output the population prediction results, and provide population data support for other modules;

[0028] S3. Manage the revenue, expenditure, budget, and deficit of local finance;

[0029] Simulate the impacts of different financial decisions such as tax adjustment and expenditure increase on economic development, output the financial simulation results, and evaluate the feasibility of financial decisions;

[0030] S4. Analyze the driving forces of socio-economic growth, including capital accumulation, technological progress, and labor supply factors, simulate the impacts of different economic decisions such as investment decisions and industrial decisions on regional development, output the socio-economic simulation results, and provide a basis for decision-making;

[0031] S5. Predict the regional energy demand, consumption, and carbon dioxide emissions, simulate the impacts of different energy decisions such as the promotion of renewable energy and the improvement of energy efficiency on the environment and economy, output the energy simulation results, and evaluate the environmental and economic benefits of energy decisions;

[0032] S6. Analyze the impact of major industries on regional development, including aspects such as output value, employment, and investment. Simulate the promoting effects of different industrial decisions such as industrial support and industrial upgrading on the regional economy, and output the industrial simulation results to provide guidance for industrial planning;

[0033] S7. Forecast regional agricultural production, considering the impact of labor, capital, and technology production factors on agricultural output, and output the agricultural simulation results to provide a basis for agricultural decision-making;

[0034] S8. Evaluate the impact of different industrial activities on the environment, including aspects such as solid waste, air pollution, and carbon emissions, output the ecological environment assessment results, and put forward environmental protection suggestions;

[0035] S9. Analyze the impact of rainfall and evapotranspiration on water production, simulate the effects of different water resource decisions such as water-saving decisions and water resource allocation on regional water resource management, and output the water resource simulation results to provide decision support for water resource management;

[0036] S10. Forecast future land use changes, considering the demand for land due to urbanization and industrialization factors, simulate the impact of different land decisions such as land protection and land use planning on regional development, and output the land simulation results to provide a basis for land planning and management;

[0037] S11. Evaluate the impact of different decisions on regional well-being, including aspects such as income, wealth distribution, living standards, and health well-being, output the regional well-being evaluation results, and provide feedback for decision adjustment and optimization;

[0038] S12. Summarize the simulation and evaluation results of each module, conduct comprehensive analysis, identify the key issues and challenges of urban sustainable development, and put forward targeted decision suggestions and decision-making plans to promote the sustainable development of the city.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention realizes module integration based on a meta-model, forming a complete and systematic evaluation framework. The modules are interrelated and can comprehensively consider multiple aspects of urban development. Based on the system dynamics method, it integrates multiple models and methods, can comprehensively simulate and predict the future development trend of the city, can evaluate the long-term impact of different strategies or decisions on urban sustainable development, and also allows users to make assumptions about the future and adjust model parameters to meet the specific needs of different cities or regions, can flexibly cope with the uncertainty and complexity of urban development. At the same time, it comprehensively considers the well-being levels in the three dimensions of economy, society, and environment, reflecting the comprehensiveness of sustainable development. Through the positive cycle of "well-being - investment - growth", it emphasizes the interaction and mutual promotion among the elements of sustainable development. Based on a large number of literature studies and empirical data, the model construction has a scientific basis and adopts a variety of advanced methods and technologies, such as the life cycle assessment method, the Douglas production function, and the AEZ agricultural production potential assessment method, improving the accuracy and reliability of the evaluation. The model is oriented to small-scale cities, integrating detailed knowledge theory details, and comprehensively evaluates the long-term development capabilities in the three dimensions of urban economy, society, and environment based on urban analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 Fig. shows a schematic diagram of the strategy framework of the urban sustainable development evaluation and simulation system of the present invention;

[0041] Figure 2 Fig. shows a schematic diagram of the energy model framework of the urban sustainable development evaluation and simulation system of the present invention;

[0042] Figure 3 Fig. shows a schematic diagram of the water resource model framework of the urban sustainable development evaluation and simulation system of the present invention;

[0043] Figure 4 Fig. shows a schematic diagram of the meta-model department structure and connection of the urban sustainable development evaluation and simulation system of the present invention;

[0044] Figure 5 Fig. shows a schematic diagram of the integration of urban analysis and system method of the urban sustainable development evaluation and simulation system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0046] Embodiment 1

[0047] Please refer to Figures 1 to 4 , the present invention proposes an urban sustainable development assessment and simulation system, which realizes module integration based on a meta-model, including a population module, a finance module, a socio-economic module, an energy module, an industry module, an agriculture module, an ecological environment module, a water resources module, a land module, and a regional well-being evaluation module;

[0048] Among them, it should be noted that the population module is used to predict future population size and structural changes, providing data support in terms of population for decision-making. The finance module is used to manage local financial revenues, expenditures, budgets, and deficits, ensuring the effective allocation of resources and economic stability, and simulating the impact of different financial decisions on economic development. The socio-economic module is used to analyze the driving forces of socio-economic growth and simulate the impact of different economic decisions on regional development. The energy module is used to predict regional energy demand, consumption, and carbon dioxide emissions, and simulate the impact of different energy decisions on the environment and the economy. The industry module is used to analyze the impact of major industries on regional development and simulate the promoting effect of different industrial decisions on the regional economy. The agriculture module is used to predict regional agricultural production. The ecological environment module is used to evaluate the impact of different industrial activities on the environment. The water resources module is used to analyze the impact of rainfall and evapotranspiration factors on water production. The land module is used to predict future land use changes to assist in planning and managing land resources. The regional well-being evaluation module is used to evaluate the impact of different decisions on regional well-being.

[0049] In this embodiment, it should also be noted that the population module considers factors such as fertility rate, mortality rate, and aging factor during the implementation process, and simulates population dynamics through the system dynamics method.

[0050] In this embodiment, it should also be noted that the driving forces analyzed by the socio-economic module include capital accumulation, technological progress, and labor supply factors.

[0051] In this embodiment, it should also be noted that the impacts analyzed by the industry module include aspects such as output value, employment, and investment.

[0052] In this embodiment, it should also be noted that the agriculture module is also used to evaluate the impact of labor, capital, and technological production factors on agricultural output.

[0053] In this embodiment, it should also be noted that the impacts evaluated by the ecological environment module include aspects such as solid waste, air pollution, and carbon emissions.

[0054] In this embodiment, it should also be noted that the water resources module is also used to simulate the effect of different water resources decisions on regional water resources management.

[0055] In this embodiment, it should also be noted that the land module is also used to simulate the impact of different land decisions on regional development.

[0056] In this embodiment, it should also be noted that the impacts assessed by the regional welfare evaluation module include income, wealth distribution, living standards, and health and well-being.

[0057] Example 2

[0058] The urban sustainable development assessment simulation system of the present invention, namely USDGSS, is implemented based on a metamodel, which is used to integrate modules including strategy, social economy, energy, water resources, agriculture, industry, and ecological environment;

[0059] Specifically, the simulation method includes the following steps:

[0060] (1) Implementation of the strategy module:

[0061] In USDGSS, strategies include population, energy, financial expenditure, and environmental protection strategies. The dynamic changes in population need to be further simulated through the population growth model. The input parameters generated by other strategies are often directly fitted through statistical models, general equilibrium models, IPAT models or elasticity formulas. The methods used for parameter quantification include linear regression, generalized linear models (such as binomial distribution, Poisson distribution), nonlinear least squares method, and Bayesian method.

[0062] The population growth model mainly considers the impact of population strategy, fertility intention, age structure and medical level;

[0064] It is worth noting that the fertility rate after the second-child decision in 2016 was lower than expected, which is mainly due to the impact of average education level on fertility intention;

[0065] Therefore, the section was redesigned to add the role of education in population growth;

[0066] At the same time, it allows users to make assumptions about the future;

[0067] The birth rate is the output of this department. The calculation of the fertility rate is based on a broad summary of relevant research results and is fitted through the Levenberg-Marquardt nonlinear least squares method to achieve the fitting of fertility strategy, average education level, and birth rate;

[0068] Education level also affects fertility rates, with higher education levels often leading to lower fertility intentions;

[0069] It can be calculated based on the number of sexually active women of childbearing age (15 to 49 years), the total fertility rate and the age-specific fertility distribution;

[0070] Factors affecting life expectancy in the model consider the level of medical care and historical experience statistics. Mortality will be directly affected by life expectancy;

[0071] The model estimates population changes based on the empirical estimated numerical relationship between life expectancy at birth and the number of women of childbearing age, birth rate, and mortality rate in each region;

[0072] The population module model adopts a top-down system dynamics approach, considering the total population, population structure, fertility rate, and mortality rate;

[0073] The fertility rate and mortality rate are represented endogenously to capture the impact of socioeconomic development on profound demographic changes;

[0074] Considering long-term models of the development process, it is necessary to handle the fertility rate and mortality rate;

[0075] The population sector is a coherent component model with 4 age groups (0 to 60 years old and above), including both men and women;

[0076] Considering the aging process, people are transferred from one age group to the next age group every year, so the model can well describe the aging trend;

[0077] For example, for the population aged 18 - 34, the equation is: Population [18 - 34 years old] = INTEG (Aging [0 - 17 years old] - Mortality [18 - 34 years old] - Aging [18 - 34 years old] (The function INTEG performs the mathematical process of integration or accumulation);

[0078] The birth population of the first stock is calculated by multiplying the number of women of childbearing age by the fertility rate;

[0079] (2) Implementation of the financial module:

[0080] In USDGSS, financial revenue is related to local industries and production capacity. At the same time, financial revenue affects the local financial expenditure budget. Local finance includes three major parts: expenditure, revenue, and deficit;

[0081] Expenditure funds expenditure, financial expenditure (including education, agriculture, forestry, animal husbandry, and fishery, deficit repayment, social welfare financial expenditure), and revenue remitted to the central government;

[0082] Expenditure funds expenditure is a function of expenditure funds revenue;

[0083] Revenue mainly comes from expenditure funds revenue and financial revenue. Expenditure funds revenue is a function of per capita wealth (quantified using per capita GRP), and financial revenue is a function of regional gross domestic product;

[0084] Deficit growth is a function of market interest rate, borrowing time, and is also related to local development decisions;

[0085] The impact of local financial expenditures on various well - being aspects is multi - faceted, covering multiple fields such as economy, society, education, health, and environmental protection;

[0086] Economically, local financial expenditures promote economic growth by investing in infrastructure construction, education, and technology fields;

[0087] For example, educational expenditures improve the quality of the labor force, promote scientific and technological progress, and thus drive sustainable and high - quality economic growth;

[0088] Socially, local financial expenditures in social welfare, such as social security, medical insurance, and subsistence allowances, help improve the living standards of people in need;

[0089] Educationally, financial expenditures in the education field improve educational facilities, enhance educational quality, and promote the development of human resources;

[0090] In terms of health care, financial expenditures in the health care field enhance the level of medical services, strengthen the public health system, and improve people's health levels;

[0091] Environmentally, financial expenditures in environmental protection help improve environmental quality, enhance resource utilization efficiency, reduce solid, liquid, and gaseous waste pollution, safeguard the regional ecological environment quality, and promote sustainable development;

[0092] In terms of social security, local financial expenditures in social security, including old - age insurance, medical insurance, and social assistance, help build a more complete social security system and improve people's sense of security and happiness;

[0093] In summary, the impact of local financial expenditures on local well - being is comprehensive and profound, not only related to the development of the local economy, but also directly related to the quality of life of the people and social stability;

[0094] In USDGSS, financial expenditures play multiple roles in providing public services, promoting economic development, reducing inequality, safeguarding basic rights and interests, supporting ecological civilization construction, optimizing the financial expenditure structure, and promoting green development;

[0095] (3) Realization of the social - economic module:

[0096] In USDGSS, the growth of agricultural land and the capital accumulation in industrial and service production are considered the main forces driving the growth of labor demand;

[0097] Technological progress, represented by the average number of years of education and the total number of R & D personnel, tends to reduce labor demand because more advanced technologies require less labor per unit of capital;

[0098] Unless the labor supply is insufficient to meet the demand, the employment level tends to adjust with the labor demand over time;

[0099] Since USDGSS is oriented towards the long term (rather than the short term), the employment algorithm used is driven by capital, average years of education, and R&D personnel, and R&D personnel or other alternative indicators represent the technological level that enterprises can adopt. These indicators can reflect the contributions of comprehensive factors such as technological progress, institutional innovation, and resource allocation efficiency in the economy to economic growth.

[0100] The growth model proposed by Robert Solow introduces three factors: capital, labor, and technological progress;

[0101] The Threshold 21 model method further improves this theory. The economic growth theory is based on labor (Lab), capital accumulation (Cap), and total factor productivity (TFP); according to this model, the increase in capital, labor, and technological progress in the capital stock will drive economic growth. Therefore, its general model is shown in Equation (1)

[0102]

[0103] In the formula, E i represents nominal GDP (the subscript 2000 represents the value in 2000);

[0104] Capital accumulation refers to the growth process of the capital stock in the economic system, mainly derived from investments in economic productive assets. Its core is to transform savings into investments to form a new capital stock. Fixed asset investment directly increases the capital stock of production tools and infrastructure.

[0105] On the one hand, relevant research shows that investment is affected by happiness. Investors with higher levels of happiness tend to show higher risk tolerance because they are less sensitive to the negative emotions of failure;

[0106] On the other hand, behavioral economics believes that people with higher levels of happiness tend to consume products or services related to high-level needs, which is based on Maslow's hierarchy of needs theory. High-level needs drive the growth of consumption and promote investment.

[0107] In addition, Charles Dow believes that current investment is determined based on expectations of future economic growth. Therefore, the derivation process of the investment model is shown in Equations (1), (2), and (3):

[0108] I i = I i-1 × TIF i (1);

[0109]

[0110] In the formula, I represents the increased fixed asset investment (the subscript 2000 represents the value in 2000), EExp represents the growth expectation, ISen represents the investment sentiment, and TIF represents the total impact of investment sentiment and economic growth expectation;

[0111] (4) Implementation of the energy module:

[0112] The use of the per capita GRP indicator as a driver is not only based on rich theories. The electricity demand is directly related to per capita wealth, which is more valuable for reference. At the same time, in the fitting and verification of regions, per capita GRP and electricity demand show a good correlation;

[0113] According to the energy demand, and based on the construction of local thermal power generation installed capacity and new energy generation capacity, calculate the power generation of fossil fuels, new energy, bioenergy, and hydrogen energy categories, and their energy supply amounts satisfy the general equilibrium principle;

[0114] In this sector, with the expected growth of future energy demand, both electricity demand and non-electricity demand will also expand. Most of the new energy is ultimately converted into electricity. Hydrogen energy and bioenergy can be converted into electricity or directly supplied in the form of heat, and nuclear energy is often converted into electricity through an indirect process;

[0115] The additional capacity required to meet the growing demand is achieved by building additional traditional thermal power plants, nuclear power plants, bioenergy, hydrogen energy, hydropower plants, and other renewable energy generation capacities;

[0116] Considering unit energy production, the carbon dioxide emissions of some new energy (photovoltaic, hydropower, wind power) are negligible compared to fossil energy combustion, and other environmental impacts are also ignored. In the model, their power generation demands and installed capacities are unified, and hydrogen energy, nuclear energy, and bioenergy are accounted for separately;

[0117] Through the prediction of socio-economic driving factors such as gross regional product (GRP), population, and industrial structure, the model measures direct emissions by referring to the carbon dioxide emission factors recommended by a large number of old IPCC documents;

[0118] The model also simulates the synergistic effect of CO2 emission reduction on the reduction of local air pollutants, including SO2, NOx, particulate matter with an aerodynamic diameter not greater than 10 μm (PM10), and particulate matter with a diameter of 2.5 μm (PM2.5);

[0119] The USDGSS model allows the phasing out of coal-fired power generation and heating units, as well as some carbon-intensive industries, while considering advanced CCS technologies to achieve ambitious emission reduction goals;

[0120] Although both bioenergy and CCS technologies are difficult to develop due to their high acquisition costs and low public awareness;

[0121] Assuming that the current local air pollutant reduction efforts remain unchanged, significant reductions in SO2, NOx, PM 10, and PM 2.5 air pollutants can be achieved solely through carbon dioxide reduction actions;

[0122] (5) Implementation of the industrial module:

[0123] In USDGSS, investment is one of the important driving forces for economic growth, which improves the productivity level, develops infrastructure, and conducts technological innovation through capital investment;

[0124] Assuming that the labor force, labor quality, and technological level in the same region are basically the same, therefore, the growth of different industries in this region is mainly affected by the investment ratio of different industries and the elasticity of investment for different industry growth;

[0125] The investment ratio is related to the region's advantageous resources and decision-making plans;

[0126] Affected by the scale effect, the environmental problems in the region are attributed to the main industries;

[0127] The development of the main industries drives the development of upstream and downstream related industries, often forming related industrial clusters, which helps to reduce costs, improve production efficiency, and enhance innovation capabilities;

[0128] Therefore, analyzing the main industrial chain or cluster helps to effectively analyze the regional environmental problems;

[0129] According to the National Economic Industry Classification (GB / T 4754—2017), the economic industries are classified into agriculture, mining, energy, and manufacturing;

[0130] In the manufacturing category, there are 31 different types. Based on expert knowledge, AI, and the industrial information database of the demonstration area, the 31 different manufacturing types are further classified into 12 major industrial categories;

[0131] According to the proportion of the regional national economic industry output value, study and determine the industries with the dominant proportion of the industrial chain output value in this region, and determine them as the main industries in this region;

[0132] The investment tendency of the main industries is reflected by the change in the proportion of industrial output value;

[0133] If the proportion of the output value of an industry is higher, it means that the investment willingness for this industry was stronger in the past;

[0134] The industrial output value is directly related to the quantity of products. At the same time, the quantity of products is directly related to the total impact of the product production process on the environment;

[0135] The proportion of the output value of the main industry in the regional industrial output value often fluctuates within a certain range. By using historical data, investment tendencies, and decision-making plans, the future trend of this industry is determined, and the development of the regional industry is envisioned.

[0136] (6) Implementation of the agricultural module:

[0137] In USDGSS, the agricultural module uses an evaluation method that combines the Douglas production function and the AEZ agricultural production potential to predict regional agricultural production.

[0138] Based on the Douglas function theory, the impact of the input of labor, capital, and technological production factors on agricultural output is studied and evaluated.

[0139] The AEZ method is a method for evaluating agricultural production potential, developed by the Food and Agriculture Organization of the United Nations (FAO).

[0140] The AEZ method evaluates the agricultural production potential of a specific region by comprehensively considering climate, soil, and land use factors, and determines the types of crops suitable for planting and the corresponding yield levels.

[0141] Agricultural output is determined by the input of production factors and the production potential calculated by the AEZ method.

[0142] Since capital investment is often directly used for soil factor improvement, land leveling, and land management;

[0143] Therefore, as a factor influencing the total impact of these factors on agricultural production, capital investment is used to avoid double counting of capital investment and these influencing factors in the method. Two sets of model formulas are summarized in the research.

[0144] Model 1: Without calculating the impact of capital investment (I) in the production function, calculate the impact of each influencing factor of AEZ, as shown in Equations (1) and (2):

[0145]

[0146] P = C × S × T × M (2);

[0147] In the formula, Y represents the crop yield (the subscript 2000 represents the value in 2000), L represents labor, T represents technology, and P represents production potential.

[0148] Model 2: Without calculating the impact of soil factors, terrain factors, and land management changes in production potential (P), calculate the impact of capital investment (I) in the production function, as shown in Equations (3) and (4):

[0149]

[0150] P = C × S 2000×T 2000 ×M 2000 (4);

[0151] In the formula, Y represents the crop yield (the subscript 2000 represents the value in 2000), I represents investment, T represents technology, and P represents production potential;

[0152] (7) Implementation of the ecological environment module:

[0153] For agriculture, mining, energy, and manufacturing (12 major industrial categories), through the life cycle assessment method, sort out the various production links and environmental impacts of the industries, and finally identify the main environmental impacts of these industries;

[0154] Generally speaking, according to the industrial chain analysis, upstream industries are often at the starting link of the production chain and rely on the extraction and processing of natural resources;

[0155] For example, mineral extraction and oil refining activities require a large amount of natural resources (such as water, land, ore) and energy;

[0156] The extraction of these resources will not only cause direct land damage, but also consume a large amount of energy and resources, bringing a large number of negative externalities, including pollution emissions;

[0157] While downstream industries are mainly solid waste and domestic waste, and are often recycled;

[0158] Research, sort out and analyze 7 major types of environmental impacts (solid waste and domestic waste, air pollution, carbon emissions, water environment quality, water resource consumption, soil erosion), and evaluate the main environmental impacts in each region;

[0159] The environmental impact is directly related to the industrial production volume. Therefore, the environmental impact assessment model is established based on the statistical model method;

[0160] At the same time, the environmental impact is related to the technical level and pollutant treatment. Based on the system dynamics method, identify the combined effects of these factors on the environmental impact;

[0161] (8) Implementation of the water resource module:

[0162] In the research, the water production is the difference between rainfall and actual evapotranspiration;

[0163] The final destinations of water production are surface water recharge, groundwater recharge, and water use;

[0164] Surface water recharge is related to runoff and changes in lake water volume;

[0165] In addition, water use is also affected by regional water transfer and reclaimed water recharge;

[0166] Research on models based on evapotranspiration and rainfall changes, analyze evapotranspiration, rainfall changes and their impacts on water production;

[0167] Rainfall replenishes groundwater through soil infiltration. However, the permeability of the soil determines whether rainwater can smoothly enter the ground, which is related to the soil characteristics of the region;

[0168] In addition, the duration of rainfall also affects rainwater infiltration. Gentle rainfall over a longer period helps rainwater gradually infiltrate into the soil to replenish groundwater, while short-term heavy rainfall is likely to form runoff and reduce groundwater recharge;

[0169] Considering that at the macro scale, soil characteristics generally do not change significantly, research is based on statistical empirical models to analyze the impacts of rainfall amount and rainfall frequency on surface water and groundwater recharge;

[0170] (9) Implementation of the land module:

[0171] The land module integrates the GeoSOS-FLUS model, which is mainly used to predict future land use changes and assist in the planning and management of land resources;

[0172] Based on the predicted future land use changes and a land use simulation tool based on Cellular Automata (CA), the model simulates the changes in land use patterns under different future scenarios;

[0173] This model introduces an uncertainty simulation mechanism. By adding random attraction factors, the adaptive ability of the model is enhanced, so as to better handle the uncertainty and complexity in future land use changes;

[0174] This model also considers various land use driving factors, such as population growth, economic development, transportation infrastructure, and decision-making factors, to analyze the changes in land use types through these driving factors;

[0175] At the same time, the model introduces a mechanism called "soft constraint" to more flexibly handle different restrictive conditions during the conversion of land use types;

[0176] (10) Implementation of the regional well-being evaluation module:

[0177] In USDGSS, research is based on the happiness economics model and related consumption and investment theories to describe the mutual feedback relationship between regional well-being and the level of regional sustainable development. Among them, regional well-being refers to the overall well-being level of social members, including aspects such as social members' income, wealth distribution, living standards, health well-being, social security, education level, employment opportunities, and fairness and justice;

[0178] The well-being of these regions is summarized into three core dimensions: environment, society, and economy. These three dimensions reflect the comprehensiveness of sustainable development, that is, development activities need to consider environmental protection, social welfare, and economic benefits simultaneously. The model is based on Maslow's hierarchy of needs theory and assumes that there are three stages in the improvement of this well-being, including the satisfaction of primary needs (regional well-being in the economic dimension) and the satisfaction of higher-level needs (regional well-being in the environmental and social dimensions).

[0179] The model quantifies the change in well-being based on the relative change degree of well-being in each dimension and establishes a positive cycle of "well-being - investment - growth".

[0180] In summary, based on the meta-model implementation module integration, the present invention forms a complete and systematic evaluation framework. The modules are interrelated with each other, can comprehensively consider multiple aspects of urban development, adopt the system dynamics method, can simulate and predict the future development trend of the city, can evaluate the long-term impact of different strategies or decisions on urban sustainable development, and also allows users to make assumptions about the future and adjust the model parameters to meet the specific needs of different cities or regions, can flexibly respond to the uncertainty and complexity of urban development. At the same time, it comprehensively considers the well-being levels of the three dimensions of economy, society, and environment, reflecting the comprehensiveness of sustainable development. Through the positive cycle of "well-being - investment - growth", it emphasizes the interaction and mutual promotion among the elements of sustainable development. Based on a large number of literature studies and empirical data, the model construction has a scientific basis and adopts a variety of advanced methods and technologies, such as the life cycle assessment method, the Douglas production function, and the AEZ agricultural production potential assessment method, which improves the accuracy and reliability of the evaluation.

[0181] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Urban sustainable development assessment simulation system, which is based on cutting-edge theory, expert knowledge, and the participation of multiple stakeholders to achieve module integration, and is characterized by: It includes policy module, industrial module, socio-economic module, energy module, water resource module, agricultural module, ecological environment module, land module and climate scenario module; The strategy module is used for the evaluation of urban development driving parameters; The industrial module is used to identify the main industries and development trends of the city in terms of local deficit, and to simulate the impact of different investment decisions on economic, social and environmental dimensions; The socio-economic module is used to analyze the driving force of socio-economic growth and simulate the impact of different economic decisions on regional development; The energy module is used to predict regional energy demand, consumption and carbon dioxide emissions, and simulate the impact of different energy decisions on the environment and economy; The industry module is used to analyze the impact of major industries on regional development and simulate the driving effect of different industrial decisions on the regional economy; The agricultural module is used to predict regional agricultural production; The ecological environment module is used to evaluate the impact of different industrial activities on the environment; The water resource module is used to analyze the impact of rainfall and evapotranspiration on water production and the feedback of economic development on water demand; The land module is used to predict future land use changes and evaluate the social and ecological effects of land use changes; The climate scenario module provides basic prediction data for future climate change; The integrated system assessment is used to analyze the sustainable development goals that different cities are concerned about.

2. The urban sustainable development assessment simulation system according to claim 1 is characterized by: In the implementation process of the strategy module, fertility rate, mortality rate, aging, and medical level factors are taken into consideration, and the dynamic changes of the population are simulated through the system dynamics method; economic development, new energy construction scale, carbon capture technology, biomass energy, and hydrogen energy development and utilization factors are taken into consideration, and changes in energy utilization structure are simulated through the partial equilibrium method and system dynamics method; the proportion and changes of different types of financial expenditures are taken into consideration, and the elasticity and impact of financial expenditures on education, medical level, and environmental protection are simulated through the partial equilibrium method and correlation analysis; land use planning, soil properties, soil fertility, and labor factors are taken into consideration, and agricultural production and water resource production capacity are simulated through the system dynamics method, agricultural ecological zone method, and Douglas production function model; investment, total factor productivity, and labor factors are taken into consideration, and economic growth is simulated through the input-output method, happiness economics model, and system dynamics method; production capacity, technology level, and emission requirements are taken into consideration, and the development of major industries and their environmental impacts are evaluated through the life cycle assessment method, input-output model, system dynamics method, and IPAT model.

3. The urban sustainable development assessment simulation system according to claim 2 is characterized in that: The driving forces analyzed by the socio-economic module include capital accumulation, technological progress, and labor supply factors.

4. The urban sustainable development assessment simulation system according to claim 3 is characterized by: The impact of the industrial module analysis includes output value, employment and investment.

5. The urban sustainable development assessment simulation system according to claim 4 is characterized by: The agricultural module is also used to assess the impact of climate, soil fertility, labor, capital, and technology production factors on agricultural output.

6. The urban sustainable development assessment simulation system according to claim 5 is characterized by: The impacts assessed by the ecological environment module include solid waste, water pollution, air pollution, and carbon emissions.

7. The urban sustainable development assessment simulation system according to claim 6 is characterized by: The water resource module is also used to simulate the effects of different water resource decisions on regional water resource management.

8. The urban sustainable development assessment simulation system according to claim 7 is characterized by: The land module is also used to simulate the impact of different land decisions on regional development.

9. The urban sustainable development assessment simulation system according to claim 8, characterized in that: The impacts assessed by the human welfare assessment module include per capita income, transfer payments, wealth distribution, living standards, health standards, urban environment, water supply and demand, and food supply and demand.

10. The urban sustainable development assessment simulation system according to claim 9, characterized in that: The energy module assessment is used to simulate the impact of different energy strategies on greenhouse gas emissions and energy consumption.

11. A method for simulating the evaluation of sustainable urban development, according to the system for simulating the evaluation of sustainable urban development according to claims 1-9, characterized in that: The following steps are involved: S1. Determine the simulation area and time period, collect and input the basic data required for each module, such as historical population data, financial revenue and expenditure records, socio-economic indicators, energy consumption data, industrial output value, agricultural output, ecological environment status, water resources, land use status and regional welfare related indicators; S2. Consider various strategies, use long-term planning, empirical statistics, random functions, and model building methods to set scenario parameters, simulate future changes in population size and structure, economic growth and major industrial development, energy structure, labor level, technology level, and land use type. Provide data support for other modules; S3. Manage local financial revenue, expenditure, budget and deficit; Simulate the impact of different financial decisions such as tax adjustments and expenditure increases on economic development, output financial simulation results, and evaluate the feasibility of financial decisions; S4. Analyze the driving force of social and economic growth, including capital accumulation, technological progress, and labor supply factors, simulate the impact of investment decisions, industrial decisions, and different economic decisions on regional development, and output social and economic simulation results to provide a basis for decision-making; S5. Predict regional energy demand, consumption and carbon dioxide emissions, simulate the impact of different energy decisions on the environment and economy for renewable energy promotion and energy efficiency improvement, output energy simulation results, and evaluate the environmental and economic benefits of energy decisions; S6. Analyze the impact of major industries on regional development, including output value, employment, and investment, simulate the role of different industrial decisions on industrial support and industrial upgrading in promoting the regional economy, output industrial simulation results, and provide guidance for industrial planning; S7. Predict regional agricultural production, consider the impact of labor, capital, and technology production factors on agricultural output, output agricultural simulation results, and provide a basis for agricultural decision-making; S8. Evaluate the impact of different industrial activities on the environment, including solid waste, air pollution, and carbon emissions, output ecological environment assessment results, and put forward environmental protection recommendations; S9. Analyze the impact of rainfall and evapotranspiration on water production, simulate water-saving decisions and water resource allocation, and the effects of different water resource decisions on regional water resource management, output water resource simulation results, and provide decision support for water resource management; S10. Predict future land use changes, consider the demand for land due to urbanization and industrialization, simulate the impact of different land decisions on land protection and land use planning on regional development, output land simulation results, and provide a basis for land planning and management; S11. Evaluate the impact of different decisions on regional well-being, including income, wealth distribution, living standards, and health and well-being, and output regional well-being evaluation results to provide feedback for decision-making adjustments and optimization; S12. Summarize the simulation and evaluation results of each module, conduct comprehensive analysis, identify key issues and challenges for sustainable urban development, and put forward targeted decision-making recommendations and solutions to promote sustainable urban development.

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