Method for constructing randomized Chinese energy economy and environment system integration model CE3METL

Abstract

The invention discloses a method for constructing a randomized Chinese energy economy and environment system integration model CE3METL. The method for constructing the randomized Chinese energy economy and environment system integration model CE3METL comprises the following steps: information data processing; the embodiment is designed. Multiple uncertainties are introduced on the basis of a model CE3METL of a Chinese version to establish a random-version CE3METL model, a solution algorithm of large-scale numerical operation under a GAMS program platform is given, aiming at the limitation of model research of multiple uncertain factors, the Monte Carlo simulation method is integrated on the basis of the comprehensive model CE3METL of a Chinese 3E system, and the method is suitable for large-scale numerical operation of the large-scale numerical operation of the large-scale numerical operation of the large-scale numerical operation of the large-scale numerical operation of the large-scale numerical operation of the large-scale numerical operation. According to the method, multiple uncertainties are introduced into a Chinese single department energy-economy-environment (3E) system integration model CE3METL under the condition that multiple uncertainties such as economic growth, energy market and technical change are fully considered, and a random version of the Chinese energy-economy-environment system integration model is constructed.

Description

Technical Field

[0001] This application relates to the modeling of comprehensive assessment of uncertain climate change, and in particular to a method for constructing a randomized China Energy, Economy and Environment System Integration Model CE3METL. Background Art

[0002] Many relevant scholars have done a lot of useful exploration work in the uncertainty theory and application frontiers of energy-economy-environment (3E) integrated system.

[0003] The economy is a major component of the economy-energy-environment integrated system, directly affecting future energy demand and the size of future carbon emissions. Economic uncertainty is therefore also an important aspect of climate-related research ( 2002; Kriegler et al., 2014), the uncertainties affecting economic growth mainly include the uncertainty of production factor inputs and their productivity, as well as the uncertainty of the elasticity of substitution between different factors. Webster et al. (2008) believed that the economic growth uncertainty parameters obtained based on historical data can avoid the subjective bias that may exist in the expert opinion method and more objectively reflect the real economic growth changes. Therefore, they used econometric forecasting methods based on historical economic growth rate data to estimate the uncertainty of future economic growth, and used a random walk process with a drift rate to simulate the future economic evolution trend.

[0004] R&D and investment uncertainty are important aspects of uncertainty research in the context of climate change. Baker (2009) constructed a stochastic advantage model using CO2 emission reduction and new energy investment as decision variables, and gave the optimal carbon tax path and technology investment path under the condition of climate loss uncertainty. Babonneau et al. (2012) used a bottom-up large-scale technology system model to explore the impact of uncertainty on energy policy evaluation. On the other hand, the article also applied the Monte Carlo simulation method to the top-down computable general equilibrium model GEMINI-E3 to study the impact of many uncertainties such as economic growth, climate loss, oil and gas prices, and carbon prices on climate policy evaluation.

[0005] The study of technological uncertainty is an important aspect related to the adjustment of energy technology structure and the evolution of carbon emission trajectory (Arrow & Fisher, 1974; Rosenberg, 1998; Baker & Shittu, 2009). Castelnuovo et al. (2003) used the ETC-RICE model to discuss the relationship between endogenous technological change and carbon emission reduction behavior under an uncertain environment. Bosetti and Tavoni (2009) examined the impact of innovation uncertainty on emission reduction policy analysis. They believed that the consideration of uncertainty can help increase investment in technological innovation and reduce the average cost of emission reduction. and Otto (2009) found that the uncertainty in the development of new supporting technologies (such as CCS technology) will significantly affect the welfare losses of residents caused by the implementation of emission reduction policies; compared with the simple method of waiting for technology to mature, introducing policies to promote technological progress in the entire economy and enhance technological externalities is a more proactive way to reduce emissions.

[0006] Uncertainty about learning effects is a major source of uncertainty in technological change, particularly in the development of carbon-free energy technologies. Generally speaking, the more mature a technology, the less uncertainty in its estimated learning rate (Route et al., 2009). Raw material costs, power plant size, fuel price fluctuations, fixed and variable operation and maintenance costs, and technological design are also important factors contributing to technological learning uncertainty (Junginger et al., 2005; Nemet, 2006). Furthermore, resource endowments, labor market fluctuations, income and taxation, transportation costs, and changes in electricity demand can all contribute to technological learning uncertainty (Route et al., 2008). From a methodological perspective, the choice of the specific functional form used to characterize learning effects is also a source of learning uncertainty. In particular, ignoring the contribution of other technological advancement factors to reducing technological costs will overestimate the true level of technological progress brought about by technological learning (Yeh & Rubin, 2012).

[0007] In summary, research work related to uncertainty in the context of climate change is mostly based on integrated assessment models (IAMs) or 3E system integration models, and is mainly carried out along the route of "economic and R&D investment uncertainty, energy and technology uncertainty, and endogenous technological progress uncertainty". From the perspective of introducing multiple uncertainties, most work only considers part of the uncertainties in the above research routes, such as economic development, energy prices, or R&D investment. Models that introduce multiple uncertainty factors at the same time are still very rare, especially models that systematically consider the uncertainty of multiple technological learning effects. Therefore, to address the above problems, a method of constructing a randomized China Energy Economy Environment System Integration Model CE3METL is proposed. Summary of the Invention

[0008] In this embodiment, a method for constructing a randomized China Energy Economy Environment System Integration Model CE3METL is provided to solve the problem that there are few models in the prior art that systematically consider the uncertainty of multiple technology learning effects.

[0009] According to one aspect of the present application, a method for constructing a randomized China Energy Economy Environment System Integration Model CE3METL is provided. The method for constructing a randomized China Energy Economy Environment System Integration Model CE3METL comprises the following steps:

[0010] (1) Information data processing;

[0011] (2) Design of implementation method.

[0012] Furthermore, the information data processing in step (1) includes objective function processing, economic module processing, energy module processing, climate module processing and uncertainty processing, and the implementation method design in step (2) includes policy scenario design, result design and simulation result analysis.

[0013] Furthermore, the objective function processes:

[0014] In CE3METL, the entire macroeconomy is assumed to be perfectly anticipated, with the goal of maximizing social welfare given given preferences. The accumulation of welfare comes from increases in per capita consumption across generations. Therefore, maximizing welfare is closely related to the dynamic consumption flow and the evolutionary path of the population. The distribution of utility across generations depends on two factors: pure time preference and marginal consumption utility (or consumption elasticity), which in turn determine the choice of discount factor for intertemporal utility accumulation. Specifically, given c as consumption and L as the total population, the model's optimization objectives are arranged as follows:

[0015]

[0016] c t =C t / L t .

[0017] Furthermore, the economic module processes:

[0018] Production in CE3METL is based on input factors such as capital K, labor L, and energy e, and is carried out in the form of a Cobb-Douglas production function. Let Y be the output. The specific expression of the production process is as follows:

[0019]

[0020] K t =(1-δ1)K t-1+I t

[0021] Among them, α and β are scale parameters, respectively characterizing the dynamic changes in input efficiency. y and p are the capital value share and the substitution elasticity between the capital-labor combination and energy, respectively. Like other 3E system comprehensive evaluation models such as DICE, it is assumed that economic output is a single composite commodity, and the flow of output includes investment, consumption (government consumption and household consumption), payment of energy costs and carbon emission costs, as well as imports and exports. For input, the population growth trajectory is exogenously given, and the capital stock is determined by optimizing the consumption flow (Nordhaus, 2007; Duane et al., 2013). Energy input mainly consists of traditional energy (fossil energy) and non-fossil energy. Energy technology progress within the economy is characterized by the spontaneous energy efficiency improvement parameter.

[0022] GDP t =Y t -EC t -AC t

[0023] C t =GDP t -I t -X t +M t

[0024] EC t +AC t =E t (PE t +PNE t )

[0025] In addition, for the Chinese 3E system integration model, the treatment of import and export boundaries and the selection of closure conditions are particularly critical, which can avoid the emergence of unreasonable economic fluctuations due to incomplete market closure. CE3METL assumes that imports and exports change with the change of GDP optimization path, which is mainly achieved by setting the upper limit of imports and the lower limit of exports.

[0026] X t ≥θ x GD P t

[0027] M t ≤θ m GDP t .

[0028] Furthermore, the energy module processes:

[0029] Based on the logistic technology diffusion model, the change in technology share over time is adjusted to the change in market share over relative price (i.e., the ratio of the benchmark technology price to the alternative technology price), and the effects of policy interventions such as carbon taxes and subsidies are taken into account. Ultimately, the evolutionary path of technology substitution depends on the intensity of policy implementation and the changes in the relative costs of technology. The energy technologies considered mainly include three carbon-containing energy technologies: coal, oil, and natural gas, and seven alternative energy technologies: biomass energy, hydropower, nuclear energy, wind energy, solar energy, tidal energy, and geothermal energy. Specifically, assuming that si is the market share of energy technology and ai is the substitution parameter, by replacing the change in time with the change in technology relative price, the multiple technology diffusion logistic model can be expressed as:

[0030]

[0031] The endogenous energy technology progress mechanism mainly refers to the single-factor learning curve method that describes the evolution of non-fossil energy technology costs. The essence of this method is that as the production scale expands, production experience or knowledge will gradually accumulate, and the accumulation of knowledge stock will in turn promote technological improvement, thereby reducing production or technology use costs. Let ci(t) and ci(0) be the costs of technology i in the tth period and the initial period, respectively. The dynamic evolution relationship of energy technology costs under the learning effect is:

[0032] Among them, bi is the learning index, which can be converted into the technical learning rate in the usual sense through the exponential relationship. Specifically, the conversion relationship between the two can be expressed as:

[0033] Knowledge Capital KD k,t Like traditional capital, depreciation effects need to be considered in the process of inter-period accumulation. Therefore, the current period's knowledge capital should be the net value of the previous period's knowledge stock after deducting the obsolete part and the sum of the new knowledge flow. The depreciation rate of knowledge capital is δ2: KD k,t =(1-δ2)KD k,t-1 +S k,t E t

[0034] The total energy price of the CE3METL model is a composite of fossil energy prices and non-fossil energy prices under the influence of carbon tax and subsidy policies. It can also be understood as a weighted energy price combination of technology share and policy effect: PF t =∑ f C f,t S f,t (1+tax f,t )

[0035] PNFt =∑ k C k,t S k,t (1-sub k,t ).

[0036] Furthermore, the climate module:

[0037] Including carbon cycle, the formation of radiative forcing flow, temperature response relationship and climate feedback damage, etc. For regional models, the climate system module is simplified and only endogenous anthropogenic carbon emissions are considered. It can be obtained by summing the carbon content of various carbon-containing energy sources and multiplying the corresponding consumption, that is: Emis t =∑ f ξ f S f E f,t

[0038] Global carbon emissions Temis t Emis is China's total carbon emissions t 、Remis carbon emissions in other parts of the world t and the sum of global exogenous natural emissions: Temis t =Emis t +Remis t +Emis0

[0039] The carbon emission stock is the accumulation of annual CO2 emissions and past emission stocks adjusted by natural deposition rates, namely: CumE t =(1-sr)CumE t +Temis t .

[0040] Furthermore, the uncertainty processing adopts Latin hypercube sampling technology to sample the above uncertain parameters and generate 2000 sample path values respectively;

[0041] ① Labor productivity, substitution elasticity, and AEEI uncertainty

[0042] Based on the characteristics of the CE3METL model, we focus on the substitution elasticity between the capital-labor combination and energy, which has a significant impact on the model results. Similar to the approach of Webster et al. (2008) and Babonneau et al. (2012), we first calibrate the substitution elasticity under the base scenario. Then, we assume a normally distributed random factor with a mean of 1 and a standard deviation of 0.3, and multiply it by the substitution elasticity under the base scenario to represent the substitution elasticity under uncertainty. Based on this, we can obtain the distribution of parameter values.

[0043] This model takes the uncertainty of exogenous energy efficiency improvement into account. First, the AEEI under the maximum likelihood scenario is determined through model calibration. Then, a random variable with a normal distribution of mean 1 and standard deviation 0.3 is assumed. The sampled value of the random variable is multiplied by the AEEI under the maximum likelihood scenario to represent the AEEI under uncertainty. This approach is similar to that of Webster et al. (2008) and Babonneau et al. (2012).

[0044] ② Uncertainty in technology learning

[0045] Consider the uncertainty of technological learning effects in the CE3METL model to incorporate the potential impact of numerical differences in learning effects on technological substitution, energy consumption, and carbon emissions trajectories into the model results and analysis;

[0046] The mean and interval boundaries of the learning rates of various alternative technologies are shown in Table 1. Based on the numerical information in Table 1, it is assumed that the learning rates of various technologies follow the corresponding uniform distribution, and a random learning rate set with a sample size of 2000 is generated for each alternative technology based on the Latin hypercube technique;

[0047] (2.3) Uncertainty in fossil energy prices

[0048] This paper uses the random geometric Brownian motion process (GBM) to characterize the uncertainty of future energy price evolution:

[0049] dP i-t =α i P i-t dt+σ i P i-t dW i-t

[0050] Where i = 1, 2, 3, P 1-t , P 2-t and P 3-t Represent the prices of coal, oil and natural gas respectively, α i is the price drift rate, σ i is the instantaneous volatility, and dW i-t It represents the increment of the standard deviation of the Wiener process, and this process obeys the normal distribution with mean 0 and variance dt.

[0051] Since the price evolution of coal, oil and natural gas is often correlated with each other, it is assumed that these correlations can be described by the following equation (Dixit and Pindyck, 1994):

[0052] Here, ρ 1-2 , ρ1-3 and ρ 2-3 is the correlation coefficient, which reflects the degree to which two energy price series deviate from their respective trends when they move simultaneously.

[0053] Furthermore, the policy scenario design: Based on current domestic and international experience, market-based emission reduction policy tools include carbon pricing (carbon trading or carbon tax) and renewable energy subsidy support policies (such as feed-in-tariff). Based on the above analysis, this paper designs three emission reduction policy models: one is a single carbon pricing policy model; the second is a single renewable energy subsidy policy model; and the third is a hybrid policy model of the two. Different policy intensities are designed for carbon pricing and renewable energy subsidy policies. Specifically, five carbon pricing levels are designed: 0,200$ / tC, 400$ / tC, 600$ / tC, and 800$ / tC, and the subsidy level is set at 0, 20%, and 40%, resulting in a total of 15 policy scenarios (3*5=15) as shown in Table 3. Based on each policy scenario, this paper systematically studies China's future macroeconomic growth, energy technology development, and especially the evolution of carbon emission paths under an uncertain environment (2010-2050).

[0054] Furthermore, the result design analysis is based on the simulation results of the evolution of total energy consumption, the simulation results of the future carbon emission evolution trend and median, 60% confidence interval, 80% confidence interval and 90% confidence interval under China's baseline scenario, and the simulation results of the future carbon intensity evolution trend and median, 60% confidence interval, 80% confidence interval and 90% confidence interval under China's baseline scenario are obtained through the analysis of the changing trend of carbon intensity.

[0055] Furthermore, the simulation results analysis includes: policy scenarios, optimal peak time distribution and distribution results of emission peak levels. Among them, the policy scenarios focus on the policy tools required to achieve carbon emission reduction and energy structure adjustment goals from the perspective of policy makers, as well as the socio-economic impacts brought about by policy implementation; the optimal peak time distribution under different policy scenarios first gives the evolution trend of China's total carbon emissions, and on this basis, the peak time of China's total carbon emissions under various policy scenarios is statistically analyzed, and finally the probability distribution result of the peak time of total carbon emissions is obtained.

[0056] Through the above-mentioned embodiments of the present application, multiple uncertainties are introduced on the basis of the Chinese version of the model CE3METL to establish a random version of the CE3METL model, and a solution algorithm for large-scale numerical calculations under the GAMS program platform is given. In view of the limitations of model research on multiple uncertainty factors, the present invention is based on the Chinese 3E system comprehensive model CE3METL, integrates the Monte Carlo simulation method, and introduces multiple uncertainties into the Chinese single-sector energy-economy-environment (3E) system integration model CE3METL, while fully considering multiple uncertainty environments such as economic growth, energy market and technological changes, to construct a random version of the Chinese energy-economy-environment system integration model. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0058] Figure 1 This is a schematic diagram of the research architecture of the CE3METL model according to an embodiment of the present application;

[0059] Figure 2 This is a schematic diagram of technology progress rate parameter estimation information for one embodiment of the present application;

[0060] Figure 3 This is a schematic diagram of energy price uncertainty parameter value assumptions for one embodiment of the present application;

[0061] Figure 4 A schematic diagram of a combined policy scenario setting for an embodiment of the present application;

[0062] Figure 5 This is a schematic diagram of the forecast results of China's future economic growth scenarios by various institutions in one embodiment of the present application;

[0063] Figure 6 This is a schematic diagram of the economic growth path from 2010 to 2050 under the baseline scenario of one embodiment of the present application;

[0064] Figure 7 A schematic diagram of the evolution path of China's total energy consumption under one embodiment of the present application;

[0065] Figure 8 A schematic diagram of the evolution path of China's total carbon emissions under the baseline scenario of an embodiment of this application;

[0066] Figure 9A schematic diagram of the evolution path of China's economic carbon intensity under the baseline scenario of one embodiment of this application;

[0067] Figure 10 This is a schematic diagram of the optimal carbon emission peak time distribution according to an embodiment of the present application;

[0068] Figure 11 This is a schematic diagram of the probability distribution of the peak carbon emission level according to an embodiment of the present application;

[0069] Figure 12 This is a schematic diagram of the distribution of cumulative carbon emissions within a given time interval according to an embodiment of the present application. DETAILED DESCRIPTION

[0070] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0071] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0072] In this application, terms such as "upper," "lower," "left," "right," "front," "back," "top," "bottom," "inner," "outer," "center," "vertical," "horizontal," "transverse," and "longitudinal" indicate positions or locations based on the positions or locations shown in the accompanying drawings. These terms are primarily intended to better describe this application and its embodiments and are not intended to limit the devices, elements, or components indicated to having a specific orientation, or to being constructed or operated in a specific orientation.

[0073] Furthermore, some of the above terms may be used to express other meanings besides indicating a position or location. For example, the term "on" may also be used to indicate a dependency or connection in certain circumstances. Those skilled in the art will understand the specific meanings of these terms in this application based on the specific circumstances.

[0074] Furthermore, the terms "installed," "disposed," "provided with," "connected," "connected," and "socketed" should be interpreted broadly. For example, they can refer to fixed connections, removable connections, or integral structures; mechanical connections or electrical connections; direct connections, indirect connections through an intermediary, or internal communication between two devices, elements, or components. Those skilled in the art will understand the specific meanings of these terms in this application based on the specific circumstances.

[0075] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0076] See also Figure 1-12 As shown, the method for constructing the randomized China Energy Economy Environment System Integration Model CE3METL includes the following steps:

[0077] (1) Information data processing;

[0078] (2) Implementation method design;

[0079] The information data processing in step (1) includes objective function processing, economic module processing, energy module processing, climate module processing and uncertainty processing, and the implementation method design in step (2) includes policy scenario design, result design and simulation result analysis;

[0080] Objective function processing: In CE3METL, it is assumed that the entire macroeconomy is perfectly anticipated, with the goal of maximizing social welfare given preferences. The accumulation of welfare comes from the increase in per capita consumption across generations. Therefore, maximizing the welfare goal is closely related to the dynamic consumption flow and the evolutionary path of the population. The distribution of utility between different generations depends on two factors: pure time preference and marginal consumption utility (or consumption elasticity), which in turn determines the choice of discount factor for intertemporal utility accumulation. Specifically, given c as consumption and L as the total population, the optimization objective of the model is arranged as follows:

[0081]

[0082] c t =C t / Lt ;

[0083] The economic module processes: Production in CE3METL is based on input factors such as capital K, labor L, and energy e, and is carried out in the form of a Cobb-Douglas production function. Let Y be the output. The specific expression of the production process is as follows:

[0084]

[0085] K t =(1-δ1)K t-1 +I t

[0086] Among them, α and β are scale parameters, respectively characterizing the dynamic changes in input efficiency. y and p are the capital value share and the elasticity of substitution between the capital-labor combination and energy, respectively. Similar to other 3E system comprehensive evaluation models such as DICE, it is assumed that economic output is a single composite commodity, and the flow of output includes investment, consumption (government consumption and household consumption), payment of energy costs and carbon emission costs, and imports and exports. For input, the population growth trajectory is exogenously given, and the capital stock is determined by optimizing the consumption flow (Nordhaus, 2007; Duane et al., 2013). Energy input mainly consists of traditional energy (fossil energy) and non-fossil energy. Energy technology progress within the economy is characterized by the spontaneous energy efficiency improvement parameter.

[0087] GDP t =Y t -EC t -AC t

[0088] C t =GDP t -I t -X t +M t

[0089] EC t +AC t =E t (PE t +PNE t )

[0090] In addition, for the Chinese 3E system integration model, the treatment of import and export boundaries and the selection of closure conditions are particularly critical. This can avoid the occurrence of unreasonable economic fluctuations due to incomplete market closure. CE3METL assumes that imports and exports change with the GDP optimization path, which is mainly achieved by setting the upper bound of imports and the lower bound of exports (Kumbaroglu et al., 2008).

[0091] X t ≥θ x GDP t

[0092] M t ≤θ m GDP t ;

[0093] The energy module processing is based on the logistic technology diffusion model, adjusting the change of technology share over time to the change of market share with relative price (i.e., the ratio of benchmark technology price to alternative technology price), and taking into account the effects of policy interventions such as carbon taxes and subsidies. Ultimately, the alternative evolution path of technology depends on the implementation strength of policies and changes in the relative costs of technologies. The energy technologies considered mainly include three carbon-containing energy technologies: coal, oil, and natural gas, and seven alternative energy technologies: biomass energy, hydropower, nuclear energy, wind energy, solar energy, tidal energy, and geothermal energy. Specifically, assuming that si is the share of energy technology in the market and ai is the substitution parameter, by replacing the change of time with the change of technology relative price, the multiple technology diffusion logistic model can be expressed as:

[0094]

[0095]

[0096] The endogenous energy technology progress mechanism mainly refers to the single-factor learning curve method that describes the evolution of non-fossil energy technology costs. The essence of this method is that as the production scale expands, production experience or knowledge will gradually accumulate, and the accumulation of knowledge stock will in turn promote technological improvement, thereby reducing production or technology use costs. Let ci(t) and ci(0) be the costs of technology i in the tth period and the initial period, respectively. The dynamic evolution relationship of energy technology costs under the learning effect is:

[0097] Among them, bi is the learning index, which can be converted into the technical learning rate in the usual sense through the exponential relationship. Specifically, the conversion relationship between the two can be expressed as:

[0098] Knowledge Capital KD k,t Like traditional capital, depreciation effects need to be considered during the inter-period accumulation process. Therefore, the current period's knowledge capital should be the net value of the previous period's knowledge stock after deducting the obsolete part and the sum of the new knowledge flow. The depreciation rate of knowledge capital is δ2:

[0099] KD k,t =(1-δ2)KD k,t-1 +S k,tE t

[0100] The total energy price of the CE3METL model is a composite of fossil energy prices and non-fossil energy prices under the influence of carbon tax and subsidy policies. It can also be understood as a weighted energy price combination of technology share and policy effect: PF t =Σ f C f,t S f,t (1+tax f,t )

[0101] PNF t =Σ k C k,t S k,t (1-sub k,t )

[0102] The climate module: For the global climate-economic integrated assessment model (IA), depicting the relationship between the climate system and the economy and energy system is one of the main tasks of the modeling. This relationship includes the carbon cycle, the formation of radiative forcing flows, the temperature rise response relationship, and climate feedback damage. For regional models, the climate system module is simplified and only endogenous anthropogenic carbon emissions are considered. It can be obtained by summing the carbon content of various carbon-containing energy sources and multiplying the corresponding consumption, that is:

[0103] Emis t =∑ f ξ f S f E f,t

[0104] Global carbon emissions Temis t Emis is China's total carbon emissions t 、Remis carbon emissions in other parts of the world t and the sum of global exogenous natural emissions: Temis t =Emis t +Remis t +Emis0

[0105] The carbon emission stock is the accumulation of annual CO2 emissions and past emission stocks adjusted by natural deposition rates, namely: CumE t =(1-sr)CumE t +Temis t ;

[0106] The uncertainties mentioned include the uncertainty of labor productivity and the elasticity of substitution between the capital-labor combination and energy, which will affect the future economic growth path; the uncertainty of the rate of improvement in traditional energy efficiency; the uncertainty of the learning effect of various alternative technologies; and the uncertainty of energy market price fluctuations. Based on existing relevant research, assumptions are made about the distribution functions and future evolution trends of the above uncertain factors. In the process of solving the model, a large-scale Monte Carlo simulation method is used to sample the uncertain factors. In order to reduce the sampling sample size and the complexity of the model calculation while ensuring that the sample samples can fully fit the assumed distribution function, the Latin hypercube sampling technique is used to sample the above uncertain parameters and generate 2000 sample path values respectively.

[0107] ① Labor productivity, substitution elasticity, and AEEI uncertainty

[0108] China's future population growth will gradually stabilize, and the uncertainty of the total population is relatively small. Therefore, the population growth trajectory is set exogenously, and labor productivity, which has a significant impact on economic growth, is selected as the main source of economic uncertainty. First, the model calibrates labor productivity under the maximum likelihood scenario. The simulated economic growth and energy consumption under this scenario are close to the actual judgment of future evolution trends. Then, a random multiplier factor with a bounded normal distribution with a mean of 1 and a standard deviation of 0.3 is assumed and multiplied by the maximum likelihood labor productivity to represent the uncertainty of future labor productivity. This approach is similar to Babonneau et al. (2012).

[0109] The elasticity of substitution between production factors reflects the flexibility of economic production to a certain extent and has a significant impact on the model results. Based on the characteristics of the CE3METL model, we focus on the elasticity of substitution between the capital-labor combination and energy, which has a significant impact on the model results (Gerlagh & van der Zwaan, 2004). Similar to the approach of Webster et al. (2008) and Babonneau et al. (2012), we first calibrate the elasticity of substitution under the base scenario. Then, we assume a normally distributed random factor with a mean of 1 and a standard deviation of 0.3. We multiply this factor by the elasticity of substitution under the base scenario to represent the elasticity of substitution under uncertainty. Based on this, we can obtain the distribution of parameter values.

[0110] This model takes the uncertainty of exogenous energy efficiency improvement into account. First, the AEEI under the maximum likelihood scenario is determined through model calibration. Then, a random variable with a normal distribution of mean 1 and standard deviation 0.3 is assumed. The sampled value of the random variable is multiplied by the AEEI under the maximum likelihood scenario to represent the AEEI under uncertainty. This approach is similar to that of Webster et al. (2008) and Babonneau et al. (2012).

[0111] ② Uncertainty in technology learning

[0112] Consider the uncertainty of technological learning effects in the CE3METL model to incorporate the potential impact of numerical differences in learning effects on technological substitution, energy consumption, and carbon emissions trajectories into the model results and analysis;

[0113] In recent years, many scholars have estimated the learning rates of different new energy technologies and obtained a wide range of estimated intervals (e.g., IEA, 2000; McDonald & Schrattenholzer, 2001; Route et al., 2009). By sorting out the estimation results of existing literature, we obtained the means and interval boundaries of the learning rates of various alternative technologies, as shown in Table 1. Based on the numerical information in Table 1, we assume that the learning rates of various technologies follow the corresponding uniform distribution, and generate a random learning rate set with a sample size of 2000 for each alternative technology using the Latin hypercube technique.

[0114] (2.3) Uncertainty in fossil energy prices

[0115] This paper uses the random geometric Brownian motion process (GBM) to characterize the uncertainty of future energy price evolution:

[0116] dP i-t =α i P i-t dt+σ i P i-t dW i-t

[0117] Where i = 1, 2, 3, P 1-t , P 2-t and P 3-t Represent the prices of coal, oil and natural gas respectively, α i is the price drift rate, σ i is the instantaneous volatility, and dW i-t It represents the increment of the standard deviation of the Wiener process, and this process obeys the normal distribution with mean 0 and variance dt.

[0118] Since the price evolution of coal, oil and natural gas is often correlated with each other, it is assumed that these correlations can be described by the following equation (Dixit and Pindyck, 1994):

[0119] Here, ρ 1-2 ,ρ 1-3 and ρ 2-3 is the correlation coefficient, which reflects the degree to which two energy price series deviate from their respective trends when they move simultaneously.

[0120] The specific energy price uncertainty parameters are summarized in Table 2 The drift rate is mainly set based on the 2014 data from the U.S. Energy Information Administration (EIA);

[0121] Policy scenario design: Based on current domestic and international experience, market-based emission reduction policy tools include carbon pricing (carbon trading or carbon tax) and renewable energy subsidy support policies (such as feed-in-tariff). Based on the above analysis, this paper designs three emission reduction policy models: a single carbon pricing policy model; a single renewable energy subsidy policy model; and a hybrid policy model of the two. Different policy intensities are designed for carbon pricing and renewable energy subsidy policies. Specifically, five carbon pricing levels are designed: 0,200 $ / tC, 400 $ / tC, 600 $ / tC, and 800 $ / tC, and three subsidy levels are set at 0, 20%, and 40%. This results in a total of 15 policy scenarios (3 * 5 = 15), as shown in Table 3. Based on each policy scenario, this paper systematically studies China's future macroeconomic growth, energy technology development, and especially the evolution of carbon emissions paths under an uncertain environment (2010-2050).

[0122] The results design analysis is based on the simulation results of the evolution of total energy consumption, the simulation results of the future carbon emission evolution trend and median, 60% confidence interval, 80% confidence interval and 90% confidence interval under China's baseline scenario, and the simulation results of the future carbon intensity evolution trend and median, 60% confidence interval, 80% confidence interval and 90% confidence interval under China's baseline scenario through the analysis of the change trend of carbon intensity;

[0123] The simulation results analysis includes: policy scenarios, optimal peak time distribution and distribution results of emission peak levels. The policy scenarios focus on the evolution of China's future carbon emissions and energy consumption under the dual influence of external policies and uncertain environment, especially the peak time and peak level of carbon emissions, as well as the possibility of peaking in different periods under uncertain conditions. In addition, it also focuses on the total energy consumption and the evolution of non-fossil energy technology, and analyzes the possibility of achieving the 20% non-fossil energy target in 2030 under uncertain conditions. From the perspective of policymakers, it focuses on the policy tools required to achieve carbon emission reduction and energy structure adjustment goals, as well as the socioeconomic impacts brought about by policy implementation; the optimal peak time distribution under different policy scenarios first gives the evolution trend of China's total carbon emissions, and on this basis, the peak time of China's total carbon emissions under various policy scenarios is statistically analyzed, and finally the probability distribution result of the peak time of total carbon emissions is obtained.

[0124] The circuits, electronic components and modules involved are all existing technologies and can be fully implemented by those skilled in the art. Needless to say, the content protected by this application does not involve improvements to software and methods.

[0125] The above description is merely a preferred embodiment of the present application and is not intended to limit the present application. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. A method for constructing a randomized China Energy Economy Environment System Integration Model (CE3METL) is characterized by: The method for constructing the randomized China Energy Economy Environment System Integration Model CE3METL comprises the following steps: (1) Information data processing; (2) Design of implementation method.

2. The method for constructing a randomized China Energy Economy Environment System Integration Model CE3METL according to claim 1 is characterized by: The information data processing in step (1) includes objective function processing, economic module processing, energy module processing, climate module processing and uncertainty processing, and the implementation method design in step (2) includes policy scenario design, result design and simulation result analysis.

3. The method for constructing a randomized China Energy Economy Environment System Integration Model CE3METL according to claim 1 is characterized by: The objective function deals with: In CE3METL, the entire macroeconomy is assumed to be perfectly anticipated, with the goal of maximizing social welfare given given preferences. The accumulation of welfare comes from increases in per capita consumption across generations. Therefore, maximizing welfare is closely related to the dynamic consumption flow and the evolutionary path of the population. The distribution of utility across generations depends on two factors: pure time preference and marginal consumption utility (or consumption elasticity), which in turn determine the choice of discount factor for intertemporal utility accumulation. Specifically, given c as consumption and L as the total population, the model's optimization objectives are arranged as follows: c t =C t / L t 。 4. The method for constructing a randomized China Energy Economy Environment System Integration Model CE3METL according to claim 1, characterized in that: The Economics module addresses: Production in CE3METL is based on input factors such as capital K, labor L, and energy e, and is carried out in the form of a Cobb-Douglas production function. Let Y be the output. The specific expression of the production process is as follows: K t =(1-δ1)K t-1 +I t Among them, α and β are scale parameters, respectively characterizing the dynamic changes in input efficiency. y and p are the capital value share and the substitution elasticity between the capital-labor combination and energy, respectively. Like other 3E system comprehensive evaluation models such as DICE, it is assumed that economic output is a single composite commodity, and the flow of output includes investment, consumption (government consumption and household consumption), payment of energy costs and carbon emission costs, as well as imports and exports. For input, the population growth trajectory is exogenously given, and the capital stock is determined by optimizing the consumption flow (Nordhaus, 2007; Duane et al., 2013). Energy input mainly consists of traditional energy (fossil energy) and non-fossil energy. Energy technology progress within the economy is characterized by the spontaneous energy efficiency improvement parameter. GDP t =Y t -EC t -AC t C t =GDP t -I t -X t +M t EC t +AC t =E t (OR t +PNE t ) In addition, for the Chinese 3E system integration model, the treatment of import and export boundaries and the selection of closure conditions are particularly critical. This can avoid the emergence of unreasonable economic fluctuations caused by incomplete market closure. CE3METL assumes that imports and exports change with the change of GDP optimization path, which is mainly achieved by setting the upper bound of imports and the lower bound of exports (Kumbaroglu et al., 2008). X t ≥θ x GDP t M t ≤θ m GDP t 。 5. The method for constructing a randomized China Energy Economy Environment System Integration Model CE3METL according to claim 1, characterized in that: The energy module processes: Based on the logistic technology diffusion model, the change in technology share over time is adjusted to the change in market share over relative price (i.e., the ratio of the benchmark technology price to the alternative technology price), and the effects of policy interventions such as carbon taxes and subsidies are taken into account. Ultimately, the evolutionary path of technology substitution depends on the intensity of policy implementation and the changes in the relative costs of technology. The energy technologies considered mainly include three carbon-containing energy technologies: coal, oil, and natural gas, and seven alternative energy technologies: biomass energy, hydropower, nuclear energy, wind energy, solar energy, tidal energy, and geothermal energy. Specifically, assuming that si is the market share of energy technology and ai is the substitution parameter, by replacing the change in time with the change in technology relative price, the multiple technology diffusion logistic model can be expressed as: The endogenous energy technology progress mechanism mainly refers to the single-factor learning curve method that describes the evolution of non-fossil energy technology costs. The essence of this method is that as the production scale expands, production experience or knowledge will gradually accumulate, and the accumulation of knowledge stock will in turn promote technological improvement, thereby reducing production or technology use costs. Let ci(t) and ci(0) be the costs of technology i in the tth period and the initial period, respectively. The dynamic evolution relationship of energy technology costs under the learning effect is: Among them, bi is the learning index, which can be converted into the technical learning rate in the usual sense through the exponential relationship. Specifically, the conversion relationship between the two can be expressed as: Knowledge Capital KD k,t Like traditional capital, depreciation effects need to be considered during the inter-period accumulation process. Therefore, the current period's knowledge capital should be the net value of the previous period's knowledge stock after deducting the obsolete part and the sum of the new knowledge flow. The depreciation rate of knowledge capital is δ2: KD k,t =(1-δ2)KD k,t-1 +S k,t E t The total energy price in the CE3METL model is a composite of fossil energy prices and non-fossil energy prices under the influence of carbon tax and subsidy policies. It can also be understood as a weighted energy price combination of technology share and policy effects: PF t =∑ f C f,t S f,t (1+tax f,t ) PNF t =∑ k W k,t S k,t (1-sub k,t )。 6. The method for constructing a randomized China Energy Economy Environment System Integration Model CE3METL according to claim 1, characterized in that: The climate module: Including carbon cycle, the formation of radiative forcing flow, temperature response relationship and climate feedback damage. For regional models, the climate system module is simplified and only endogenous anthropogenic carbon emissions are considered. It can be obtained by summing the carbon content of various carbon-containing energy sources and multiplying the corresponding consumption, that is: Amis t ∑ f ξ f S f AND f,t Global carbon emissions Temis t Emis is China's total carbon emissions t 、Remis carbon emissions in other parts of the world t The sum of global exogenous natural emissions is: You are welcome. t =Buy t +Oars t +Emis0 The carbon emission stock is accumulated from the annual CO2 emissions and the past emission stock adjusted by the natural deposition rate, that is: HowE t =(1-sr)HowE t +Themis t 。 7. The method for constructing a randomized China Energy Economy Environment System Integration Model CE3METL according to claim 1, characterized in that: The uncertainty processing adopts Latin hypercube sampling technology to sample the above uncertain parameters and generate 2000 sample path values respectively; ① Labor productivity, substitution elasticity, and AEEI uncertainty First, the model calibrates labor productivity under the maximum likelihood scenario. The simulated economic growth and energy consumption under this scenario are close to the actual predictions of future evolution trends. Then, a random multiplier factor with a bounded normal distribution of mean 1 and standard deviation 0.3 is assumed and multiplied by the maximum likelihood labor productivity to represent the uncertainty of future labor productivity. This treatment is similar to Babonneau et al. (2012). The elasticity of substitution between production factors reflects the flexibility of economic production to a certain extent and has a significant impact on the model results. Based on the characteristics of the CE3METL model, we focus on the elasticity of substitution between the capital-labor combination and energy, which has a significant impact on the model results. Similar to the approach of Webster et al. (2008) and Babonneau et al. (2012), we first calibrate the elasticity of substitution under the base scenario. Then, we assume a normally distributed random factor with a mean of 1 and a standard deviation of 0.

3. We multiply this factor by the elasticity of substitution under the base scenario to represent the elasticity of substitution under uncertain conditions. Based on this, we can obtain the distribution of parameter values. This model takes the uncertainty of exogenous energy efficiency improvement into account. First, the AEEI under the maximum likelihood scenario is determined through model calibration. Then, a random variable with a normal distribution of mean 1 and standard deviation 0.3 is assumed. The sampled value of the random variable is multiplied by the AEEI under the maximum likelihood scenario to represent the AEEI under uncertainty. This approach is similar to that of Webster et al. (2008) and Babonneau et al. (2012). ② Uncertainty in technology learning Consider the uncertainty of technological learning effects in the CE3METL model to incorporate the potential impact of numerical differences in learning effects on technological substitution, energy consumption, and carbon emissions trajectories into the model results and analysis; The mean and interval boundaries of the learning rates of various alternative technologies are shown in Table 1. Based on the numerical information in Table 1, it is assumed that the learning rates of various technologies follow the corresponding uniform distribution, and a random learning rate set with a sample size of 2000 is generated for each alternative technology based on the Latin hypercube technique; (2.3) Uncertainty in fossil energy prices This paper uses the random geometric Brownian motion process (GBM) to characterize the uncertainty of future energy price evolution: dP i-t =a i P i-t dt+σ i P i-t dW i-t Where i = 1, 2, 3, P 1-t , P 2-t and P 3-t Represent the prices of coal, oil and natural gas respectively, α i is the price drift rate, σ i is the instantaneous volatility, and dW i-t It represents the increment of the standard deviation of the Wiener process, and this process obeys the normal distribution with mean 0 and variance dt. Since the price evolution of coal, oil and natural gas is often correlated with each other, it is assumed that these correlations can be described by the following equation (Dixit and Pindyck, 1994): Here, ρ 1-2 , ρ 1-3 and ρ 2-3 is the correlation coefficient, which reflects the degree to which two energy price series deviate from their respective trends when they move simultaneously. The specific energy price uncertainty parameters are summarized in Table 2 . The drift rate is mainly set based on the 2014 data of the U.S. Energy Information Administration (EIA).

8. The method for constructing a randomized China Energy Economy Environment System Integration Model CE3METL according to claim 2, characterized in that: Policy scenario design: Based on current domestic and international experience, market-based emission reduction policy tools include carbon pricing (carbon trading or carbon tax) and renewable energy subsidy support policies (such as feed-in-tariff). Based on the above analysis, this paper designs three emission reduction policy models: a single carbon pricing policy model; a single renewable energy subsidy policy model; and a hybrid policy model of the two. Different policy intensities are designed for carbon pricing and renewable energy subsidy policies. Specifically, five carbon pricing levels are designed: 0,200 $ / tC, 400 $ / tC, 600 $ / tC, and 800 $ / tC, and three subsidy levels are set at 0, 20%, and 40%. This results in a total of 15 policy scenarios (3 * 5 = 15), as shown in Table 3. Based on each policy scenario, this paper systematically studies China's future macroeconomic growth, energy technology development, and especially the evolution of carbon emissions paths under an uncertain environment (2010-2050).

9. The method for constructing a randomized China Energy Economy Environment System Integration Model CE3METL according to claim 2, characterized in that: The result design analysis is based on the simulation results of the evolution of total energy consumption, the simulation results of the future carbon emission evolution trend and median, 60% confidence interval, 80% confidence interval and 90% confidence interval under China's baseline scenario, and the simulation results of the future carbon intensity evolution trend and median, 60% confidence interval, 80% confidence interval and 90% confidence interval under China's baseline scenario through the analysis of the changing trend of carbon intensity.

10. The method for constructing a randomized China Energy Economy Environment System Integration Model CE3METL according to claim 2, characterized in that: The simulation results analysis includes: policy scenarios, optimal peak time distribution and distribution results of peak emission levels. The policy scenarios focus on the policy tools required to achieve carbon emission reduction and energy structure adjustment goals from the perspective of policymakers, as well as the socioeconomic impacts brought about by policy implementation; the optimal peak time distribution under different policy scenarios first gives the evolution trend of China's total carbon emissions, and on this basis, the peak time of China's total carbon emissions under various policy scenarios is statistically analyzed, and finally the probability distribution result of the peak time of total carbon emissions is obtained.

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