CN116244941A

CN116244941A - Park carbon emission accounting method

Info

Publication number: CN116244941A
Application number: CN202310164983.1A
Authority: CN
Inventors: 庞博; 李楠; 王天朋; 张玉明
Original assignee: Tianjin Sanyuan Electric Information Technology Co ltd
Current assignee: Tianjin Sanyuan Electric Information Technology Co ltd
Priority date: 2023-02-24
Filing date: 2023-02-24
Publication date: 2023-06-09

Abstract

The invention provides a park carbon emission accounting method, which comprises the following steps: s1, accounting a carbon emission object in a park and carrying out characterization definition; s2, defining a carbon emission accounting range of a park; s3, constructing a park carbon emission accounting method; s4, constructing a DSGE model structure and setting scenes. The invention has the beneficial effects that: the research framework of the industrial park carbon emission accounting method is provided, the emission factor precision is improved, the accounting mode is studied deeply, the DSGE accounting model is utilized to calculate the total carbon emission amount and the structure, and therefore the park carbon emission scientific emission under the double-carbon background is realized in a boosting mode.

Description

Park carbon emission accounting method

Technical Field

The invention belongs to the field of carbon emission data statistics, and particularly relates to a park carbon emission accounting method.

Background

With the gradual increase of the enterprise residence rate of the industrial park, the contribution rate of the carbon emission of the park in the country continuously rises, the park is used as an important platform for promoting innovation and development and gathering of various industrial enterprises, is used as an important source place for manufacturing main battlefield and greenhouse gas emission of the country, depends on different regional characteristics, leads the industry to different dominant, focuses on related element resources accurately, is expected to become a new breakthrough point for realizing pollution reduction and carbon reduction of the park through differential and accurate carbon emission accounting model research, and the assessment of the carbon emission level of the park is directly influenced by a carbon emission accounting method, so that the method is a basis for formulating an effective carbon emission reduction strategy. Therefore, it is very important and necessary to research and implement effective emission reduction means for the campus carbon emission accounting model.

Disclosure of Invention

In view of the above, the present invention is directed to a method for accounting carbon emissions in a campus, which solves at least one of the problems in the background art.

In order to achieve the above purpose, the technical scheme of the invention is realized as follows:

a method for accounting carbon emissions from a campus, comprising the steps of:

s1, accounting a carbon emission object in a park and carrying out characterization definition;

s2, defining a carbon emission accounting range of a park;

s3, constructing a park carbon emission accounting method;

s4, constructing a DSGE model structure and setting scenes.

Further, in step S1, firstly, an accounting object is defined, two characterization modes of "carbon dioxide amount" and "carbon dioxide equivalent" are distinguished, the energy structure of the industrial park is reflected by using the carbon dioxide amount, and different emission processes and environmental effects of the park are quantified by using the carbon dioxide equivalent.

Further, in step S2, the campus carbon emission accounting is divided into three Scope, reflecting the direct emission of the campus and the indirect emission of the upstream and downstream related enterprises, respectively.

Further, in step S3, the accounting process specifically includes the following parameters:

fossil fuel combustion CO2 emissions: refers to CO2 emissions generated by intentional oxidation processes of fossil fuels for energy utilization purposes;

Net purchase power implies CO2 emissions: refers to CO2 emission generated in the power production link corresponding to the net purchased power consumed by enterprises;

activity level: indicating the amount of human activity that the business would cause some carbon dioxide emissions or scavenging, such as the amount of consumption of various fuels, raw material usage, product yield, amount of outsourced electricity, amount of outsourced steam;

emission factor: quantifying a coefficient of carbon dioxide emissions or scavenging per unit activity level, the emissions factor typically being obtained based on a sampling measurement or statistical analysis, representing a representative emission rate or scavenging rate for a certain activity level under given operating conditions;

carbon oxidation rate: representing the rate at which carbon in the fuel is oxidized during combustion, indicative of the sufficiency of fuel combustion;

the specific accounting method is as follows: accounting and counting total carbon dioxide emission of a park, wherein the total carbon dioxide emission comprises two parts of fossil fuel combustion CO2 emission and CO2 emission implicit by net purchase power, and the total carbon dioxide emission of enterprises is added according to the following formula:

wherein:

-total carbon dioxide emissions of the enterprise, in tCO2;

-CO 2 emissions of fossil fuel combustion of enterprises, in the unit of tCO2;

-CO 2 emissions implicit in the net purchase of electricity by the enterprise, in tCO2;

1) Fossil fuel combustion CO2 emissions

The CO2 emission of fuel combustion is mainly calculated based on the combustion amount of fossil fuel of different varieties, the carbon content of unit fuel and the carbon oxidation rate, and the formula is as follows:

in the method, in the process of the invention,

-CO 2 emissions of fossil fuel combustion of enterprises, in the unit of tCO2;

i—the type of fossil fuel;

AD _i fossil fuel variety i is explicitly used as the fuel combustion consumption in tons for solid or liquid fuels and in tens of thousands Nm3 for gaseous fuels;

DD _j -carbon content of fossil fuel i in tC/t fuel for solid and liquid fuels and tC/ten thousand Nm3 for gaseous fuel;

OF _i -the carbon oxidation rate of the fossil fuel i is in the range of 0-1;

2) Implicit CO2 emissions from net purchased power

The power purchased by the park is hidden in CO2 emission, and the accounting formula is as follows:

in the method, in the process of the invention,

AD _{electric power} -clean purchase for enterprisesThe unit of power consumption is MWh;

EF _{electric power} -CO 2 emission factor for the power supply, in the unit tCO2/MWh.

Further, in step S4, the DSGE model construction method is as follows:

based on the traditional Ke Budao Klace production function, the energy consumption is regarded as an intermediate input product, so that the influence of the production behavior of an intermediate commodity producer on environmental pollution is measured, meanwhile, in order to better explore the relation between the environment and economy, the efficiency coefficient is introduced into labor and energy use, and the final production function is set as follows:

Wherein Yt represents the current production; ak. aL (ak+aL < 1) represents the production elasticity of capital and labor, respectively, aM=1-aK-aL represents the production elasticity of energy; η t L represents the current-period labor efficiency, ηtm represents the current-period energy efficiency; at represents the current production technology level, a random variable, which is assumed to be subject to the AR (1) process as follows:

log A _t ＝ρ _A log A _t-1 +ε _A，t ，ε _A，t ～N(0，σ _A ² )

wherein pA represents the autoregressive parameter of the technical impact, i.e., the duration of the technical impact; epsilon A and t represent random errors of technical impact and obey independent normal distribution;

η _t ^L ＝1-(η ₀ +η ₁ ST _t +η ₂ ST _t ² )

wherein STt represents the current contaminant inventory; three parameters of eta 0, eta 1 and eta 2 determine the influence of the pollutant stock on the labor efficiency coefficient;

the contaminant inventory will have an accumulation over time, assuming that any two phases of contaminant inventory follow the following relationship:

ST _t ＝(1-δ _Z )ST _t-1 +Z _t

wherein δZ represents the contaminant inventory depreciation rate;

assuming that there is a close relationship between the improvement of the energy efficiency technology and the usage amount of the energy element, and the improvement of the energy efficiency technology is an increasing function of the usage amount of the energy element, the two have the following relationship:

wherein γm represents an elastic coefficient of energy efficiency, reflecting an effective degree of energy usage to energy efficiency improvement; qt represents the energy efficiency level, i.e. the level of technology that improves energy efficiency in a "dry middle school" process, assuming it is subject to the AR (1) process as follows:

log q _t ＝ρ _q log q _t-1 +ε _q，t ，ε _q，t ～N(0，σ _q ² )

Wherein ρq represents an autoregressive parameter of the energy efficiency impact, i.e., the duration of the energy efficiency technical impact; εq, t represent the random error of energy use efficiency impact, obey independent normal distribution;

the total production costs faced by a campus, in addition to the three production element costs of capital, labor, and energy, also include the cost of reducing carbon emissions generation, then the lagrangian function of the cost minimization problem faced by the campus during production can be defined as:

YP＝Y _t +λ _t ^YP (W _t L _t +R _t K _t-1 +P _t ^M M _t +P _t ^Z Z _t +CE _t )

wherein lambda tYP represents Lagrangian multiplier corresponding to the enterprise consumption dynamic equation; CEt represents the current carbon emission cost, and the reference practice is set as follows:

CE _t ＝-ΛμM _T [(1-er _t )ln(1-er _t )+er _t ]

wherein Λ represents an enterprise carbon emission reduction cost coefficient;

the capital stock, labor force, energy and enterprise emission reduction effort are respectively biased to obtain the following first-order conditions:

/>

based on the variability of enterprises producing intermediate products and the monopoly competitiveness of the intermediate product market, the price viscosity of Calvo is introduced for enterprises to

Adjusting the price of the intermediate product to an optimal price level P, then the enterprise faces the following pricing problem:

the following two variables are defined:

the relative price is biased, and the following first-order conditions are adopted:

Wherein ε represents the replacement elastic coefficient between different intermediate commodities; pi represents inflation;

after optimal intermediate commodity pricing is achieved, the total price level may be determined by the following equation:

1＝(1-ω)(P _t ^* ) ^1-ε +ωπ _t ^ε-1 。

further, the DsGE model solving method is as follows:

the steady state of three elements of capital stock, labor and energy are calculated as:

/>

the relationship among the steady state values of the pollutant storage amount, the energy consumption amount and the carbon emission amount can be obtained by the following formula:

Z _ss ＝μM _ss

Z _ss ＝δ _Z ST _ss 。

further, the scheme discloses an electronic device, which comprises a processor and a memory, wherein the memory is in communication connection with the processor and is used for storing executable instructions of the processor, and the processor is used for executing.

Further, the present solution discloses a server comprising at least one processor and a memory communicatively coupled to the processor, the memory storing instructions executable by the at least one processor, the instructions being executable by the processor to cause the at least one processor to perform a campus carbon emission accounting method.

Further, the present solution discloses a computer readable storage medium storing a computer program which when executed by a processor implements a campus carbon emission accounting method.

Compared with the prior art, the method for accounting the carbon emission of the park has the following beneficial effects:

the invention provides a park carbon emission accounting method, which provides a research framework of the industrial park carbon emission accounting method, aims to improve the accuracy of emission factors, and further researches accounting modes and utilizes a DSGE accounting model to calculate the total carbon emission amount and structure so as to assist in realizing scientific emission of park carbon emission under a double-carbon background.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. In the drawings:

FIG. 1 is a schematic diagram of a technical roadmap for studying a model of carbon emission accounting of a park in a two-carbon background according to an embodiment of the invention;

FIG. 2 is a schematic diagram of setting of parameter indicators under different situations according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of energy consumption and energy intensity according to an embodiment of the present invention;

FIG. 4 is a schematic diagram of energy consumption in industrial parks 2019-2030 under different industrial structure scenarios according to embodiments of the invention;

FIG. 5 is a schematic diagram of CO2 emissions from industrial parks 2019-2030 in three scenarios according to embodiments of the present invention;

FIG. 6 is a schematic diagram of CO2 emissions from different industrial institutions in industrial parks 2019-2030 in three situations according to embodiments of the invention;

fig. 7 is a schematic diagram of energy saving and emission reduction rates of each scenario in 2030 compared with a reference scenario according to the embodiment of the invention.

Detailed Description

It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.

The invention will be described in detail below with reference to the drawings in connection with embodiments.

2.1 campus carbon emission accounting object and characterization definition

The basis for determining the accounting object of the campus is from two paths, namely, the greenhouse gas types listed in official documents such as international convention, standard, government documents and the like, and the specific accounting object is definitely combined with the actual carbon emission source of the campus. Some authoritative documents internationally may provide carbon emissions accounting object references. The carbon emission sources of the industrial park are divided into two types, wherein the type I is based on emission departments and is divided into (1) raw material exploitation; (2) producing agricultural products; (3) energy supply; (4) buildings and infrastructure; (5) industrial production; (6) transportation; (7) waste treatment; (8) and (5) greening the landscape. Type II is based on the emission process and is divided into (1) primary energy combustion; (2) secondary energy use; (3) other chemical processes (production and processing links, incineration and landfill, and sewage treatment processes). The carbon sink is the greening landscape plant in the park. When determining the type of greenhouse gas emissions, type I, type II and the greenhouse gas have a hierarchical structure, type II can be regarded as a conversion unit of type I in the clear of greenhouse gas emissions, and the gas emissions are directly related to the individual components of type II. Wherein, the exhaust gas types accounting for energy consumption (primary energy combustion and secondary energy use) are carbon dioxide (CO 2), methane (CH 4) and nitrous oxide (N2O), and mainly comprise carbon dioxide (CO 2); during the production process, the greenhouse gas species accounted for may include carbon dioxide (CO 2), methane (CH 4), nitrous oxide (N2O), chlorofluorocarbons (CFCS), sulfur hexafluoride (SF 6), depending on the production process; greenhouse gases emitted during incineration, landfill and sewage treatment typically include carbon dioxide (CO 2), methane (CH 4).

In the aspect of the representation of the carbon emission accounting result, the method is divided into two cases (1) taking the carbon dioxide amount as the representation of the result; (2) characterized by the carbon dioxide equivalent. When the carbon emission accounting object is mainly carbon dioxide only based on energy consumption, the accounting result is characterized as carbon dioxide representation; if methane (CH 4) and nitrous oxide (N2O) are emitted in consideration of energy consumption, the accounting result is characterized as carbon dioxide equivalent. In addition, when considering the emission of greenhouse gases from various process sources (including waste gas and solid waste treatment, production and processing, etc.), the conversion is carried out according to different global warming potentials due to the variety of gases, and the gases are uniformly characterized in terms of carbon dioxide equivalent.

When the carbon emission accounting results are read, the carbon emission accounting results are characterized by reflecting the carbon emission conditions of the park from different angles. The carbon emission accounting result characterized by the carbon dioxide is mainly used for reflecting the fossil energy consumption structure of the park so as to identify the key emission process and emission source and identify the high-carbon-emission energy use type. However, due to the different global warming potentials of greenhouse gases (SF 6 > PFCS > HFCS > N2O > CH4 > CO 2), the types of greenhouse gases produced and the emission effects are not the same from one emission department to another. Thus, the use of carbon dioxide equivalent can effectively characterize a variety of carbon emission processes and their environmental effects. For example, in industrial parks, large-scale electronic equipment and instrument production, electric power production, magnesium product processing and the like are carried out, SF6 gas is discharged, or sugar industry is taken as a main material, CH4, N2O and other gases are associated in the process of corn growth-processing-fertilizer production and sewage treatment in the production process, so that various heterogeneous greenhouse gas discharge amounts caused in the production process need to be considered, and the carbon dioxide equivalent value is more suitable for characterization.

2.2 carbon emission accounting Range definition for park

The definition of the carbon emission accounting range originates from the value chain research of enterprises and products. The world resource institute and the world sustainable development industry and commerce council provide an accounting range for measuring the greenhouse gas emission of an enterprise value chain and products in an enterprise value chain (Scope 3) standard published at the end of 2011, and the accounting range is specifically divided into 3 Scope (1) Scope1 comprises direct emission generated in the combustion process and the chemical production process of the enterprise value chain; (2) scope2 contains indirect emissions from electricity, steam, heating or cooling purchased by the business; (3) scope3 is any indirect emission produced by the enterprise value chain outside Scope2, including upstream emission of products purchased by the enterprise, shipping emission, downstream emission after use. In recent years, three-layer sub-accounting ranges are directly expanded from enterprise scale to city scale and country scale. Wherein Scope1, all direct emission processes, mainly refer to greenhouse gas emission processes occurring within the geographic boundaries of the checklist; scope2, an indirect discharge process due to purchase of electricity, heat supply and external regulation; scope3, all other indirect emissions not covered by Scope2 (including fuel, building materials, machinery, food, water resources, clothing, etc., produced and transported from outside the city). While industrial parks have a certain difference between enterprise and city scale, developing carbon emission accounting. As an enterprise consortium, the carbon emissions of a campus typically involve a value chain of multiple enterprises, with interactions or even symbiotic relationships, and accounting coverage is more complex than that of a single enterprise. Currently, researchers have divided the scope of carbon emission campaign research accounting associated with campuses. However, the above investigation of the carbon emission accounting range of the park is focused on Scope1 and Scope2, and the carbon emission level of the park cannot be fully reflected yet.

In summary, the range of carbon emission accounting for the campus is summarized as follows:

scope 1-all direct discharge processes (including local power, heat supply; industrial production; local solid waste treatment, etc.) that occur inside the campus boundaries.

Scope2 off-shore electricity and thermally induced carbon emissions.

Scope3 any indirect emissions generated outside Scope2, including indirect emissions such as upstream emissions and shipping emissions of purchased products from various businesses and authorities in the campus.

2.3 study of carbon emission accounting method in park

(1) Fossil fuel combustion CO2 emissions

Refers to CO2 emissions generated by intentional oxidation processes of fossil fuels for energy utilization purposes.

(2) Implicit CO2 emissions from net purchased power

Refers to CO2 emission generated in the power production link corresponding to the net purchased power consumed by enterprises.

(3) Activity level

Refers to the amount of human activity that an enterprise may cause some carbon dioxide emissions or scavenging, such as the amount of consumption of various fuels, the use of raw materials, the production of products, the amount of electricity purchased, the amount of steam purchased, and the like.

(4) Emission factor

A coefficient of carbon dioxide emissions or scavenging per unit activity level is quantified. The emission factor is typically obtained based on a sampling measurement or statistical analysis, representing a representative emission rate or clearance of a certain activity level under given operating conditions.

(5) Carbon oxidation rate

Refers to the rate at which carbon in the fuel is oxidized during combustion, and characterizes the sufficiency of fuel combustion.

(6) Accounting method

The accounting and statistics of the total carbon dioxide emission amount of the park of the research institute comprises two parts of fossil fuel combustion CO2 emission and CO2 emission implicit by net purchase power, and the total carbon dioxide emission amount of enterprises is added according to the following formula.

Wherein:

-total carbon dioxide emissions of the enterprise, in tCO2;

-CO 2 emissions of fossil fuel combustion of enterprises, in the unit of tCO2;

-CO 2 emissions implicit in the net purchase of electricity by the enterprise, in tCO2.

1) Fossil fuel combustion CO2 emissions

in the method, in the process of the invention,

-CO 2 emissions of fossil fuel combustion of enterprises, in the unit of tCO2; />

i—the type of fossil fuel;

OF _i -the carbon oxidation rate of the fossil fuel i is in the range of 0-1.

2) Implicit CO2 emissions from net purchased power

in the method, in the process of the invention,

AD _{electric power} The unit of the power consumption is MWh, which is the power consumption of the net purchase of enterprises;

In summary, the industrial park carbon emission accounting method can be further researched from the following three aspects of (1) improving the accuracy of the emission factors, deeply investigating the fuel type, combustion technology and the like of the region where the industrial park is located, and more accurately calculating the carbon emission factors. The research work needs local government, standardization organization and related scientific research institutions to cooperate, and research is conducted on the aspects of energy types, combustion technology and the like used in the industrial park, so that an authoritative and credible carbon emission factor data platform is built. (2) Bottom-up and top-down algorithms are improved. On one hand, carrying out mass flow and life cycle analysis of more park production processes, and collecting microscopic data; on the other hand, the regional economic structure is considered, the input-output table is reasonably compiled, and the aim is to realize the mixed accounting of the carbon emission of the park. (3) And establishing a dynamic carbon emission accounting model to predict the total carbon emission and the structure, thereby realizing effective accounting and control of the carbon emission of the industrial park.

2.4 DSGE model structure and scenario set

(1) Introduction to DSGE model

DSGE represents a framework for analysis of dynamic random general equalization, a macroscopic economy based on general equalization theory and dynamic optimization theory. Models with general equity (general equity), randomness (stochastics) and dynamicity (dynamics) characteristics established based on the analysis framework can be called DSGE models, wherein general equity refers to the fact that behaviors of various economic subjects such as a park, an enterprise and a government are mutually influenced through optimal behavior decision, but all markets and the economic subjects are simultaneously in an equilibrium state; the randomness refers to that under the optimal behavior decision of an economic main body, the economic main body generates random behavior through random disturbance of exogenous impact, so that uncertainty in reality and economy and society is realized; the dynamic property means that the optimal behavior decision of each economic subject can change along with time, so that the decision of each economic subject can consider not only the current influence but also the future influence.

The DSGE model was originally traced back to the real economic cycle model (RBC) proposed by Kydland and Prescott in 1982, with which authors studied the current united states economic society and suggested that the main cause of the united states economic fluctuation was from technical impact. RBC model as the earliest version of DSGE model that does not contain an inefficiency setting, has mainly the following three features: firstly, irrespective of the existence of a currency mechanism; secondly, considering the effect of exogenous technology random impact on economic growth; thirdly, importance is attached to the effectiveness of the economic cycle.

From this point on, on the basis of RBC models, various economic bodies (parks, enterprises, governments, financial institutions, etc.), markets (commodity markets, labor markets, financial markets, etc.), inefficiency settings (price viscosity, wage viscosity, information viscosity, etc.), exogenous impacts (technical impacts, policy impacts, etc.), DSGE models (New Keyensian Dynamic Stochastic General Equilibrium, NK-DSGE) under the new coann theoretical framework were formed. The DSGE model has been developed rapidly since creation, and has not been built as a standard model so far, and is developed and modified in different degrees and aspects on the basis of the core three-way of the new Kann model, but there are several recognized and classical DSGE models, such as Christino et al and closed economic models of Smets and Wouters. The former incorporates many modeling elements such as variable capital utilization, working capital loan mechanisms, investment adjustment costs, etc.; the latter uses bayesian to estimate model parameters and introduces price viscosity, payroll viscosity settings, etc., both of which become important references and starting points for the subsequent scholars to build the DSGE model. Through the development of the last forty years, DSGE models have become an important method for studying macro economic growth, economic fluctuations and simulating macro policies.

Admittedly, the DSGE model has become the main stream analysis framework of modern macro economics, not only being compelled by academic world, but also being widely applied to the research of government economy and financial institutions, such as the institutions of OECD national center, chinese people's bank, etc. Gali et al believe that its rapid development is due to the nature of the macroscopic economic phenomenon itself and the definition of the DSGE analysis framework. The DSGE analysis framework has three advantages:

one is three features of the DSGE model itself (general equalization, randomness and dynamics). Firstly, macroscopic economic phenomena show complex dynamic properties, and a static model cannot well describe economic phenomena such as cross-period consumption decision, investment, interest rate and the like in macroscopic economy; secondly, the economy itself or the economy behavior main body is subjected to various uncertainty impacts in the macro economy, and only random models can model the uncertainties well; finally, macro economic phenomena are the result of the mutual connection of all economic behavior subjects, and the local equilibrium is unable to describe the interaction between all subjects, so that analysis is required under a general equilibrium framework. In conclusion, the model constructed based on the general balance, randomness and dynamic characteristics enables the model simulation result to achieve higher degree of similarity with the actual macroscopic economy.

And secondly, the combination of microscopic and macroscopic economic analysis. When the DSGE model is constructed, the behavior decision of a microscopic economic main body is firstly described in detail to obtain a corresponding behavior equation, and then the corresponding behavior equation is converted into a macroscopic behavior equation. By the method, microscopic and macroscopic economic analysis is combined, and the defect that the traditional macroscopic economic model cannot analyze microscopic economy is avoided.

Thirdly, a dynamic optimization theory is adopted. And calculating a corresponding optimal decision behavior equation when the effectiveness, profit or target of economic bodies such as parks, enterprises, government and the like under corresponding constraint conditions is maximized through a dynamic optimization theory. Meanwhile, the model parameters are set based on basic driving force behind economy, and have structural characteristics and are not influenced by Lucas criticique, so that the theoretical effect of the optimal economic policy can be better analyzed.

(1)DSGE model construction

The theoretical basis of the DSGE model is to describe the behavior of the economic body from bottom to top, so that the economic body in the explicit model is the first step in constructing the DSGE model. Typically, economic entities in the DSGE model relate to three types of economic entities, namely, a campus, an enterprise, and a government, and may also relate to other economic entities, such as environmental departments, central banks, and financial institutions, depending on the research direction. In addition, different kinds of economy subjects can be further subdivided, such as enterprises can be further subdivided into final commodity enterprises, intermediate commodity enterprises and the like.

After determining the economic subject to be studied, a second step of modeling can be developed, namely describing the behavior decision-making manner of each microscopic economic subject with mathematical equations. The behavior decision of the microscopic economic subject can be expressed as a mathematical thinking that the economic subject maximizes or minimizes the objective function of the economic subject under certain technical, resource or information constraints. In the actual economy society, because the social properties of different economy subjects are different, the restrictions to which the economy subjects are subjected and the purposes of behavior decision are greatly different, for example, the purpose of behavior decision of a campus subject is to maximize the utility, and the restrictions to which the economy subjects are subjected are budget restrictions; the aim of the behavior decision of the enterprise main body is to maximize profit or minimize cost, and the constraint is technical constraint or market demand constraint; the action decision of the government body aims at maximizing the social benefit level, and the constraint is resource constraint or information constraint. In general, these optimization problems can be solved by using the Lagrangian multiplier method, and the behavior decision equations of different economic subjects can be calculated.

After determining the behavior decision mode of the micro economic main body, developing a third step of constructing a model, namely deriving a macroscopic behavior equation based on the behavior equation of the micro economic main body. When the same class of economic bodies are assumed to have the same preference, namely the same homogeneity characteristics, macroscopic behavior equations of the class of economic bodies can be obtained through a direct summation mode, and in general, the equation form obtained through the direct summation is similar to the equation form before the direct summation; when it is assumed that the same class of economic agents do not have the same preference, i.e. have heterogeneous characteristics, a simple and straightforward summing method is not suitable for constructing macroscopic equations of behavior for the economic agents, in which case the overlap method (OverlappingGenerations, OLG) is typically used to sum microscopic behaviors. The DSGE model is combined with the macro by deducting from a micro equation to a macro equation, so that a solid economic theoretical basis is provided, and consistency of micro and macro economic analysis is ensured.

After the three steps are completed, the last step of building the model, namely setting of the clearing condition, can be carried out. The general balance characteristic of the DSGE model is realized by setting a clearing condition, wherein the clearing condition mainly comprises the following two aspects, namely, the market clearing is achieved, namely, the supply requirements of all production elements in the market are kept balanced; secondly, the financial clearance of the economic main body is achieved, namely, the income and expenditure of each economic main body need to be balanced.

Based on the results achieved by a large number of scholars in macro economic policy research, existing scholars have introduced DSGE models into the research of environmental related policy mechanisms and achieved a certain result research. Therefore, the research refers to relevant documents based on DSGE model research environment policy effect at home and abroad, and constructs an environment dynamic random general equalization (Environmental Dynamic Stochastic GeneralEquilibrium, E-DSGE) model capable of evaluating carbon emission reduction policy effect in China. Because the E-DSGE model needs to consider the existence of environmental factors, the construction of the E-DSGE model is more complex compared with the construction of the conventional DSGE model, firstly, the basic assumption and innovation of the E-DSGE model are provided; secondly, a behavior decision equation of each economic main body in the E-DSGE model and an equilibrium equation under clear conditions are adopted; thirdly, calculating the analytic solution of each variable in a stable state.

1) Basic assumptions of model construction.

The conventional DSGE model is mainly divided into an RBC-DSGE model based on a real economic cycle theory and an NK-DSGE model based on a new Kane theory. The E-DSGE model constructed in the project is mainly constructed by taking an NK-DSGE model as a basis and referring to related models of other scholars. The model construction involves basic assumptions of two aspects, specifically as follows:

firstly, in view of the fact that heavy industry and chemical industry still occupy a large proportion in the current industrial structure of China, the supply of green technology, green products and the like is insufficient, the mode of consuming energy to generate carbon dioxide is still dominant in the production of industrial parks of China, and therefore the influence of other atmospheric pollutants and other factors is not considered in the model, and meanwhile, carbon emission in the production process is regarded as a main pollutant affecting the environmental quality. Meanwhile, carbon emission generated by production activities affects not only the output and profits of the campus enterprises, but also the inter-period utility.

And secondly, adopting a closed economic rule containing nominal viscosity characteristics in an NK-DSGE model. The equilibrium state of the economy in the model consists of the behavioral decisions of four economic bodies, environmental departments, parks, enterprises and governments, respectively. Wherein the enterprises are of two types, namely an intermediate commodity producer in a monopolized competitive market environment and a final product producer in a fully competitive market environment; the government establishes association with the campus and the enterprise through tax and currency mechanisms, and establishes a carbon emission reduction policy through an action environment department; the environmental department reflects the carbon emission reduction mechanism of China. Regarding the setting of the nominal viscosity, the nominal price viscosity is mainly described by the pricing mode of Calvo.

2) Innovations in model construction.

The E-DSGE model constructed in the project is innovated in individual aspects, and is specifically as follows:

firstly, an NK-DSGE model under a new Kanden theory framework is selected as a basic model, and adjustment and modification are carried out on the basis. The nominal price viscosity and the capital adjustment cost factors are introduced into the model setting, so that the economic theoretical framework of the model is full under the condition of meeting the research requirement of the carbon emission reduction policy effect, and only the possible research result is kept consistent with the actual situation of the actual economy society.

And secondly, the carbon emission mechanism and the DSGE model are combined as much as possible. The method is mainly characterized in that the method is interlinked through two aspects of work, one of the two aspects of work is used for describing a carbon emission mechanism in enterprises and environmental departments based on the negative effect of carbon emission on the production behaviors of the enterprises, and the modeling modes of Heutel, annichlarico and Dio are referenced, wherein the carbon emission mechanism is mainly realized through 3 equations, and the distribution is a relation equation between carbon emission and energy consumption in the environmental departments and the degree of effort of enterprise emission reduction, a production function equation and a carbon emission cost equation, wherein the production function equation causes labor efficiency loss due to carbon emission in the enterprise departments; secondly, setting different carbon emission reduction policy situations, and simulating different carbon emission reduction policy mechanisms by adding corresponding exogenous factors.

Thirdly, the rebound effect of energy sources is considered. Based on research results of Shao Shuai and the like and based on a learning by going theory, an energy efficiency technical mechanism is introduced in the production behavior of an enterprise, so that the research of the carbon emission reduction policy effect under the energy technical impact is realized.

3) And (5) constructing a model.

The project builds a general balanced model of macro environment with large dynamic random of a park enterprise, introduces environmental variables such as carbon emission, environmental quality, pollutant storage and the like, and researches the influence of carbon emission reduction policies on macro economy and environment.

In view of the fact that energy consumption is a main source of carbon emission in the practical economy and society, enterprises need to put into not only basic elements such as labor and capital, but also resource elements such as energy during production. Therefore, the project regards the energy consumption as an intermediate input product on the basis of the traditional Ke Budao glas production function (only considering the existence of labor and capital elements), thereby measuring the influence of the production behaviors of intermediate commodity manufacturers on environmental pollution. Meanwhile, in order to better explore the relation between environment and economy, efficiency coefficients are introduced into labor and energy use. The final production function specific form reference, etc., is set as:

Wherein Yt represents the current production; ak. aL (ak+aL < 1) represents the production elasticity of capital and labor, aM=1-a K-aL represents the production elasticity of energy sources, respectively; η t L represents the current-period labor efficiency, ηtm represents the current-period energy efficiency; at represents the current production technology level, a random variable, which is assumed to be subject to the AR (1) process as follows:

logA _t ＝ρ _A log A _t-1 +ε _A，t ，ε _A，t ～N(0，σ _A ² )

wherein ρA represents an autoregressive parameter of the technical impact, i.e., the duration of the technical impact; εA, t represents the random error of the technical impact, subject to an independent normal distribution.

This study assumes that the labor efficiency coefficient is closely related to contaminant inventory, set by reference Zheng Lilin and Zhu Qigui [44] to:

η _t ^L ＝1-(η ₀ +η ₁ ST _t +η ₂ ST _t ² )

wherein STt represents the current contaminant inventory; three parameters η0, η1, η2 determine the effect of the contaminant inventory on the labor efficiency coefficient.

ST _t ＝(1-δ _Z )ST _t-1 +Z _t

where δZ represents the contaminant inventory depreciation rate.

At present, the improvement of energy efficiency is attributed to the action of external factors, such as policy promotion, in the articles for researching energy efficiency, but in the actual society, the improvement of energy efficiency is more promoted by internal factors, such as innovation and development of energy technology, price mechanism of energy market, and the like. Therefore, in order to make the model more in line with the actual situation, the study introduces the endophytic growth theory into the energy efficiency setting of the model, and carries out the endophytic treatment on the energy efficiency improving mechanism.

One endogenous growth theory idea adopted in the research is the idea of "dry middle school (Learning by doing)" proposed by Arrow in 1962, which considers that enterprises can accumulate experience in the production and investment processes, and when experience accumulation reaches a certain degree, the production technology is improved, so that the use efficiency of capital is improved, and finally the reduction of capital rewards is reduced. In short, the "dry middle" idea considers the increase in technical experience (stock) as an increasing function of capital increase (stock). Then, when the "dry middle school" idea is applied to the use process of the energy elements, we can consider that experience and knowledge for improving the energy efficiency can be obtained from the energy use in the production process, and when the related experience and knowledge are accumulated to a certain extent, the energy efficiency technology of the enterprise can be improved, so that the energy efficiency is comprehensively improved. Therefore, based on the relationship among the production technology, knowledge experience and capital in the idea of "in the dry middle school", the present study assumes that there is a close relationship between the improvement of the energy efficiency technology and the usage of the energy elements, and that the improvement of the energy efficiency technology is an increasing function of the usage of the energy elements, and the two have the following relationship:

log q _t ＝ρ _q log q _t-1 +ε _q，t ，ε _q，t ～N(0，σ _q ² )

wherein ρq represents an autoregressive parameter of the energy efficiency impact, i.e., the duration of the energy efficiency technical impact; εq, t represent the random error of the energy use efficiency impact, subject to an independent normal distribution.

CE _t ＝-ΛμM _t [(1-er _t )ln(1-er _t )+er _t ]

where Λ represents an enterprise carbon emission reduction cost coefficient.

/>

the following two variables are defined:

wherein ε represents the replacement elastic coefficient between different intermediate commodities; pi represents inflation.

1＝(1-ω)(P _t ^* ) ^1-ε +ωπ _t ^ε-1

(2) DSGE model solving

Based on the above analysis, the equilibrium path of the economics assumed by the present study has been known, the next step being to calculate the steady state. The steady state of the model refers to the state where the solution of the model eventually approaches, with the random term taking its mean (typically zero mean). Assuming that the economy is in a zero draft expansion state when reaching a steady state, wherein the steady state value of the technical impact is 1, the carbon emission effort level of the park is 0, and the steady state values of the variables can be solved according to the first-order conditions.

The steady state of three elements of capital stock, labor and energy can be calculated as:

Z _ss ＝μM _ss

Z _ss ＝δ _Z ST _ss

based on the above calculations, analytical solutions of the main variables of the research model have been calculated, and then, based on the calculated analytical solutions, steady-state values of the respective variables are solved by Matlab software.

(3) Model parameter calibration and Bayesian estimation

For a dynamic random general equalization model, the parameter selection of the model directly affects the simulation result of the model on an economic system, so that the parameter calibration assignment is a very important link in the simulation analysis research by using the model. Liu in the book of dynamic random general equalization model and application thereof, it is introduced that the parameters of the DSGE model can be divided into two types, and by virtue of such arrangement, the E-DSGE model parameters are also divided into two types in the study: one is a parameter which can represent the steady state characteristics of the model, and because the model is an environment dynamic random general balance model, the parameters can be further subdivided into economic parameters and environment parameters, wherein the economic parameters are general parameters in a macroscopic economic model and are usually set by adopting a calibration method; the other is that the dynamic characteristic parameters of the model under exogenous impact can be displayed, and in view of the nonlinear relation between the parameters and variables which can be directly observed by the practical economic species, the parameters cannot be obtained in a direct mode, and the parameters are usually determined by adopting an estimation method.

The Calibration method (Calibration) is based on the fact that a direct linear relation exists between the parameters to be estimated and the observable variables, and parameter values can be estimated by a direct assignment mode without considering parameter distribution, so that the Calibration method is essentially an empirical assumption; in the Estimation method (Estimation), the actual statistics or the observed data are used to estimate the parameters, so as to obtain an estimated value, and the estimated value is obtained by the parameter distribution, so that the estimated value also contains information such as mean value, variance and the like. The current mainstream estimation methods are two types, namely maximum likelihood estimation and Bayesian estimation, wherein the maximum likelihood function maximizing parameter in the statistical data is regarded as an estimated value of the parameter to be estimated, and the posterior distribution is obtained by supposing prior distribution and correcting the data, and the average value of the posterior distribution is regarded as a posterior estimated value of the parameter to be estimated. The research adopts a literature collection method to calibrate and assign basic parameters of the model, and adopts a Bayesian estimation method to estimate related parameters reflecting dynamic characteristics of the model.

The parameters of the dynamic random general equalization model are conventional parameters, namely parameters which are frequently found in the research of macroscopic economic problems, and the values of the conventional parameters are not greatly different from most of documents, and even consensus is achieved. Therefore, the present study refers to the parameters which are already present in the relevant literature, the parameters in the present model are assigned through comparison and calibration, and the basic parameters which need to be calibrated mainly comprise: economic parameters and environmental parameters.

1) Economic parameter

The economic parameters of the model mainly comprise: the cash register factor beta, the elasticity coefficients alpha K, alpha L, alpha M of each production element, the commodity replacement elasticity epsilon, the investment adjustment cost coefficient theta, the commodity price viscosity parameter omega, the consumption tax rate tau C, the labor tax rate tau L, the capital tax rate tau K, the capital depreciation rate delta K and the like. Several main economic parameter implications and their calibration results will be described in detail below.

Economic parameter calibration table

2) Environmental parameters

The environmental parameters of the model mainly comprise: the absorption rate h of the natural environment on carbon dioxide, the weight ωZ of the environmental pollution in the park utility function, the labor efficiency equation coefficients eta 0, eta 1 and eta 2, the elastic coefficient gammam of energy efficiency, the emission factor mu and the pollutant stock depreciation rate delta Z.

Regarding the absorption rate h of carbon dioxide by the natural environment, this parameter indicates the self-purifying ability of the natural environment. Zhu Jun this ability to represent the natural environment by setting a persistence parameter for the environmental quality and calculating the absorption rate of the environment to carbon dioxide to be 0.1 based on the evolution process of the environmental quality, in addition to this, angelopoulos et al and Yang Ao set a similar parameter to be 0.1 in their study, so this study calibrates this parameter to be 0.1.

Environmental parameter calibration table

The basic parameters are calibrated above, and other parameters are estimated below, mainly by adopting a Bayesian estimation method. When estimating parameters, bayesian estimation firstly supposes priori distribution of the parameters according to experience, then combines actual statistical data to find posterior distribution of the parameters, and finally the estimated value of the parameters is equal to the average value of the posterior distribution.

In the parameter estimation of the DSGE model, to avoid the occurrence of random singularities (stochastic singularity) problems, it is necessary to limit the number of observable variables, which cannot be greater than the number of random disturbance impacts. Because the third chapter model sets five exogenous impacts, the study is to select four endogenous variables (total output, consumption, investment, and expansion rate of the currency) as observed variables.

After the required observation data is collected, a priori distribution of the parameters to be estimated is assumed. The prior distribution types of parameters are more, such as Beta distribution, gamma distribution and the like, and the prior distribution of the first-order autoregressive parameters of each impact in the model is designed to be Beta distribution mainly referring to the researches of Smets and Wouters, gerali and the like. Since the model constructed in the reference is not identical to the model constructed in the present study, the prior distribution of the impact parameters is not completely formulated according to the reference, but rather some adjustments are made. Since the autoregressive parameters of the impact represent the duration of the impact, the prior average of the autoregressive parameters of the energy efficiency technical impact is set to 0.5 and the prior average of the autoregressive parameters of the production technical impact is set to 0.8, considering that the duration of the energy efficiency technical impact is less than the duration of the production technical impact.

The prior distribution of the different impact parameters is set specifically as follows: the prior distribution of the first-order autoregressive parameters of the policy impact is Beta distribution with the mean value of 0.8 and the standard deviation of 0.1; the prior distribution of the first-order autoregressive parameters of the horizontal impact of the production technology is Beta distribution with the mean value of 0.8 and the standard deviation of 0.1; the prior distribution of the first-order autoregressive parameters of the energy efficiency technical level impact is Beta distribution with the mean value of 0.5 and the standard deviation of 0.1.

Bayesian estimation result of dynamic parameters

The prior distribution and the posterior mean of the parameter to be estimated can be deviated through the table, which indicates that the parameter is properly adjusted by utilizing the observation data in the Bayesian estimation process. The autoregressive parameters of exogenous impact represent the duration of the impact on the economy, so under the same prior distribution assumption, the duration of the impact of three carbon emission reduction policies on the economy is not greatly different and is about 40 periods, wherein the duration of the impact of the carbon intensity policy on the economy is minimum, and the duration of the impact of the carbon total quantity control policy on the economy is maximum. The autoregressive parameters of the two technical impacts have large difference in estimated values due to the difference of prior distribution. An autoregressive parameter estimate of the production technique impact is 0.8147, and the impact duration is about 40 periods; the energy efficiency technique impacted an autoregressive parameter estimate of 0.2107, with an impact duration of around 25 days.

(4) Model impact

To clarify economic and environmental effects generated by carbon emission reduction policies, the main variables are divided into two types, namely economic and environmental variables, wherein the economic variables to be analyzed have six variables of overall production, consumption, investment, capital, labor force and energy sources; the environmental variables to be analyzed include six variables of carbon emissions, carbon intensity values, emission reduction effort, carbon emission reduction costs, pollutant inventory, and environmental quality. The model constructed by the research mainly sets exogenous random impact from two aspects of a decarburization policy tool and a carbon emission reduction technology, wherein the decarburization policy tool comprises three exogenous random impacts of a carbon total amount control policy, a carbon strength policy and a carbon tax policy; the carbon emission reduction technology comprises two exogenous random impact technologies, namely a production technology and an energy efficiency technology. Production technology impact refers to the increase of total output when production efficiency is improved, and the production technology impact is realized mainly through impact production technology level variables in a model; the energy efficiency technical impact refers to the increase of total output when the energy efficiency is improved, and the energy efficiency technical impact is realized mainly through impact energy efficiency level variables in a model.

(5) Context setting

Because GDP, energy intensity, industry structure, energy structure and the like are all important factors influencing the long-term energy consumption and carbon emission of an industrial park, the research needs to analyze the long-term energy consumption and carbon emission of the industrial park under different conditions on the basis of applying LEAP software, and the future development situation of the industrial park is set to 3 situations, namely a reference situation, a development situation and an enhanced emission reduction situation by reasonably thinking about the future development of the industrial park and by setting related parameters and quantifying the parameters one by one. The reference year is 2018, and the prediction period is 2019-2030.

1) Basic Scenario (BS)

The base scenario is a reference scenario as a contrast to other scenarios. In this scenario, the GDP acceleration and the industrial structure are set according to data of the industrial park for nearly 3 years. In addition, it is further assumed that the energy demand and the carbon emission naturally develop on the basis of the past without taking any policy or measure of energy conservation and emission reduction, and the energy intensity are changed according to the average change rate of 2016-2018 on the basis of the base year.

2) Development Scenario (DS)

The development scenario is to analyze the energy saving and emission reduction potential of the industrial park by combining the current energy consumption situation of the industrial park, and take corresponding policy measures aiming at climate change, such as industrial structure optimization, technical improvement of high-energy-consumption industry and the like. GDP speed-up is properly slowed down to 13%; the industrial structure and the energy structure are optimized to be 65:35 and 25:30:5:32:8 respectively, and the energy intensity is reduced to 0.644 t/ten thousand yuan.

Setting of parameter indicators in different situations of FIG. 2

3) Enhanced Emission Reduction (ERS)

The DS scenario of the study is that under the condition that the development of the industrial park is not seriously influenced, the GDP speed increase of the industrial park is further slowed down to 10 percent under the circumstance; further optimizing industrial structure and energy structure, introducing energy-saving and environment-friendly technology, developing clean energy with great force, and the like, wherein the energy intensity is further reduced to 0.617 t/ten thousand yuan respectively at 50:50 and 30:27:5:25:13.

(6) Conclusions and suggestions

1) Industrial park long-term energy consumption

a. Influence of economic acceleration on energy demand

And analyzing the total energy consumption and the energy intensity of the industrial park under the situation of 3 GDP acceleration by using the LEAP model. The larger the GDP speed is, the larger the total energy consumption amount is, the larger the corresponding CO2 emission amount is, and the lower the energy intensity is. In 2030, the total energy consumption under the conditions of BS, DS and ERS is 1047 ten thousand t,813 ten thousand t and 572 ten thousand t respectively, and the total energy consumption is increased by 2 times, 1.47 times and 0.7 times respectively compared with the standard 2018; the energy intensity is 0.598, 0.637 and 0.618, which are respectively reduced by 47.36%, 43.93% and 45.6% compared with the standard year 2018. From the predicted overall result, the industrial park does not take powerful control measures and policies under the situation of BS, and develops according to the rough economic development mode, and the total energy consumption of the industrial park tends to increase for a long time, and the situation is not suitable for the industrial park to walk on a low-carbon development road.

Under the DS situation, the industrial park has a linear rising trend year by year through optimization of an industrial structure, technical improvement, innovation of a process flow and change of an energy structure, but the rising trend is smaller than that of the BS scene and larger than that of the ERS scene. In this scenario, the industrial park is in a medium and high speed economic development mode. In ERS scenario, the industrial park further optimizes the energy structure and develops the third industry to the greatest extent, and betters the influence of the industrial structure on the energy demand. The reuse of the energy, the optimization of the energy structure, the optimization of the industrial structure, the improvement of the energy-saving technology and the innovation of the process flow. However, the industrial GDP speed increase in this case is significantly slowed down, and the development is limited, which is contrary to the economic development of the industrial park-driven region and the urban construction.

b. Influence of industrial structures on energy demand

The energy consumption of the second industry and the third industry under the conditions of BS, DS and ERS are 590 ten thousand, 418 ten thousand, 342 ten thousand, 472 ten thousand, 211 ten thousand and 360 ten thousand respectively. The results show that the third industry of the industrial park is developing at a high speed, and the energy consumption proportion of the third industry is rising year by year. The second industry mainly uses the non-renewable energy sources such as coal, fuel oil and the like, and the result of the energy consumption of the second industry and the third industry in 2030 year under 3 scenes can more reasonably optimize the structures of the second industry and the third industry of the industrial park, and more reasonably reduce the long-term energy consumption of the industrial park.

2) CO2 emission of industrial park

Under the basic situation, the industrial park does not take any policy for carbon emission, the CO2 emission amount is in an annual rising trend, and if the situation is developed according to the situation, the CO2 emission amount of the industrial park is always increased. Under the situation, the CO2 emission of the 2030 industrial park is 2610 ten thousand t, which is 2 times longer than the standard year, wherein the CO2 emission of the second industry is 1470 ten thousand t, and the third industry is 1044 ten thousand t. In the state where the current climate environment conditions are so severe, it is necessary to walk through a low-carbon development road, and it is apparent that such a development scenario is unsuitable.

Under the development situation, the industrial park controls the economic development speed, simultaneously optimizes the energy structure and the industrial structure, introduces advanced technology, and reduces the carbon emission by 22% compared with 2030 in the reference situation although the CO2 emission also shows a trend year by year; wherein the second industry discharge is 851 ten thousand t, and the third industry is 1176 ten thousand t. Under this kind of sight, industrial structure and energy structure are more reasonable, when industry garden economy obtains faster development, also have contributed to strength to energy saving and emission reduction, and this kind of sight is fit for industry garden and walks low carbon economy development road.

Under the condition of enhanced emission reduction, the CO2 emission of the industrial park has the same trend as that of the development scene, but the emission is lower than that of the development scene, and the carbon emission is reduced by 31% in 2030 year. While the economic development of the industrial park is lower and more environment-friendly under the situation, the implementation of the situation is difficult due to the fact that not only is the industrial structure and the energy structure greatly optimized by the industrial park, but also a great deal of advanced technology, advanced process flows and the like are required to be introduced, and the industrial park faces a great challenge. These three scenarios are analyzed comprehensively, wherein the development scenario is the most likely low-carbon economic development road for the industrial park to walk in the future.

3) Relevant suggestions

Based on the analysis of the energy consumption of the industrial park for nearly 3 years, the LEAP model is constructed, the results of the energy consumption and the carbon emission of the industrial park in 2019-2030 are analyzed by combining 3 scenes, and countermeasure measures suitable for carbon emission reduction of the industrial park are provided, which mainly comprise the following points:

a. the economic growth mode is changed.

The transition to economic growth is a central factor in pulling industrial park economic growth and reducing carbon emissions. The 3 scene analysis results show that the industrial park always develops economy at a high speed in the basic scene, and the economic growth speed is slowed down in the development scene and the enhanced emission reduction scene, which plays a great role in reducing the total energy consumption and the total carbon emission of the industrial park, so that the economic growth speed of the industrial park on a low-carbon development road should be properly controlled.

b. And adjusting the industrial structure.

According to 3 scene analysis results, the industrial park takes powerful measures to develop a third industry greatly under the enhanced emission reduction scene, and the method has great contribution to energy consumption and carbon emission reduction of the industrial park. Therefore, the industrial park is a necessary choice for adjusting the industrial structure while walking on the low-carbon economic development road.

c. And converting the energy structure.

Through the analysis of 3 scenes, under the reference scene, the industrial park does not adopt any policy to control carbon emission, the phenomenon that coal and fuel oil occupy a larger proportion in an energy structure is continuously carried out, the carbon emission is increased year by year, and compared with the development scene and the enhanced emission reduction scene, the energy structure is optimized, the specific gravity of clean energy is increased, and new energy sources such as natural gas, solar energy and the like can change the carbon emission trend of the industrial park. The energy consumption and the carbon emission of industrial parks in 2019-2030 tend to rise year by year.

In the development scenario, the responsibility to the environment, the ecological system and even the whole human is realized while the moderate increase of the economy is maintained, and the emission of CO2 is reduced. The total energy consumption of 2030 is 813 ten thousand t standard coal, which is turned over by 1.47 times compared with 339 ten thousand t standard coal in 2018; the emission of CO2 in 2030 is 2029 ten thousand t, which is 1.4 times higher than that of standard coal in 2018 with the emission of CO2 in 846 ten thousand t.

And the energy-saving and emission-reducing measures are effectively implemented while the economy and moderate development are carried out, the second and third industrial duty ratios are balanced, and the method is an effective way for completing the control targets of the long-term energy consumption and the CO2 emission of the industrial park. Under the policy of the green building to be promoted greatly, an intelligent energy utilization system is built by means of the internet technology, an energy detection management center is built, and automation and intellectualization of energy operation are improved. The prediction result in the development scenario can be used as a reference basis for specifying constraint indexes in the energy development planning of the industrial park, and the mode in the scenario is a long-term development road of the industrial park at present or even in the future.

d. Specifying the main object

According to the measure of the impact effect of different carbon emission reduction policies, the economic effect and the environmental effect generated by the impact of different carbon emission reduction policies are found to be different and have more obvious differences, for example, the carbon total amount control policy and the carbon strength policy have larger negative influence on the economic development, but the environment is well improved, and the carbon tax policy limits the degree of environmental improvement, but has limited influence on the economic development, so that a certain selection space is reserved for setting the carbon emission reduction macro regulation policy in China. In view of the fact that the carbon emission reduction policy is difficult to improve the environment and increase the macroscopic economy, in the case that the carbon emission reduction target and the environmental management requirement are urgent, for example, the environmental problem is too serious to be solved in a short period of time, and the carbon total amount control policy or the carbon intensity policy should be selected to enable the environmental quality and the carbon emission to be reduced to the greatest extent; under the urgent conditions of carbon emission reduction targets, environmental management requirements and economic development requirements, the selection of carbon tax policies is more favored, and the main targets are determined to be the minimum influence on economic fluctuation under the conditions of maintaining certain environmental quality and ensuring certain carbon emission reduction targets, so that environmental problems are improved and large fluctuation on economy is avoided.

e. Promote the progress of technology

According to the measure of impact effect of different technologies, it is found that whatever technical progress leads to an increase in carbon emissions and a decrease in environmental quality within a certain time frame, but the extent of carbon emissions and environmental quality decrease is smaller than the promotion effect on campus consumption and enterprise output, and technical progress is still capable of promoting environmental improvement in the long term over time, so that promotion of technical innovation and progress is still necessary and very important. From the technical category, different technical advances have different effects on economy and environment, for example, production technical advances greatly improve output, but environmental quality and carbon emission reduction are also not slightly negatively affected, and energy efficiency technical advances limit the rising amplitude of output, but well control the negative effect of technical advances on environment. Therefore, the choice of the technical category should be emphasized by the push of technical progress in view of the economic and environmental co-developments. According to the research conclusion, the technology for improving the energy efficiency can remarkably reduce the negative effects of technical progress on environmental quality and carbon emission, so that the government should increase the research and development investment and support on related environmental protection technologies such as energy use efficiency technology and the like, and the enterprises are encouraged to update the related environmental protection technologies by improving the supporting strength of the environmental protection industry or providing corresponding preferential policies.

f. Raising environmental awareness

Through model robustness inspection, along with the improvement of environmental awareness of park enterprises, the promotion effect of carbon emission reduction policies on environmental improvement can be greatly enhanced. Excessive carbon emission not only permeates in the production of each enterprise, but also drives the profit pulse of the enterprise and further influences the development of the future economic environment of the country, so that the phenomenon of carbon emission is considered to be urgent. Macroscopically, the country should make the top layer design, formulated the relevant carbon emission reduction policy, control the carbon emission from the source through the system; in the middle view, the propaganda effect of media is fully exerted, the supervision and reporting effect of masses are exerted, the punishment force is increased on a park for illicitly discharging carbon dioxide, and a sustainable low-carbon environment is created; microcosmically, the environment-friendly product is used and promoted by education and culture park, and the burden of the environment is not increased as much as possible on the premise of ensuring that the life quality is not reduced.

Those of ordinary skill in the art will appreciate that the elements and method steps of each example described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the elements and steps of each example have been described generally in terms of functionality in the foregoing description to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

In the several embodiments provided in this application, it should be understood that the disclosed methods and systems may be implemented in other ways. For example, the above-described division of units is merely a logical function division, and there may be another division manner when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted or not performed. The units may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment of the present invention.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention, and are intended to be included within the scope of the appended claims and description.

The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.

Claims

1. A method for accounting carbon emissions from a campus, comprising the steps of:

s2, defining a carbon emission accounting range of a park;

s3, constructing a park carbon emission accounting method;

s4, constructing a DSGE model structure and setting scenes.

2. The method according to claim 1, wherein in step S1, an accounting object is defined first, two characterization modes of "carbon dioxide amount" and "carbon dioxide equivalent" are distinguished, the energy structure of the industrial park is reflected by the carbon dioxide amount, and different emission processes and environmental effects of the park are quantified by the carbon dioxide equivalent.

3. The method of carbon emission accounting for a campus of claim 1, wherein in step S2, the carbon emission accounting for the campus is divided into three Scope reflecting direct emission from the campus and indirect emission from the upstream and downstream related enterprises, respectively.

4. The method for accounting carbon emissions in a campus of claim 1, wherein in step S3, the following parameters are specifically included in the accounting process:

Wherein:

-total carbon dioxide emissions of the enterprise, in tCO2;

-CO 2 emissions of fossil fuel combustion of enterprises, in the unit of tCO2;

1) Fossil fuel combustion CO2 emissions

in the method, in the process of the invention,

-CO 2 emissions of fossil fuel combustion of enterprises, in the unit of tCO2;

i—the type of fossil fuel;

OF _i -the carbon oxidation rate of the fossil fuel i is in the range of 0-1;

2) Implicit CO2 emissions from net purchased power

in the method, in the process of the invention,

5. The method for accounting carbon emissions in a campus of claim 1, wherein in step S4, the DSGE model construction method is as follows:

wherein Yt represents the current production; ak. aL (ak+al < 1) represents the production elasticity of capital and labor, respectively, am=1-aK-aL represents the production elasticity of energy; ηtl represents the current-period labor efficiency, ηtm represents the current-period energy efficiency; at represents the current production technology level, a random variable, which is assumed to be subject to the AR (1) process as follows:

log A _t ＝ρ _A log A _t-1 +ε _A，t ，ε _A，t ～N(0，σ _A ² )

wherein ρA represents an autoregressive parameter of the technical impact, i.e., the duration of the technical impact; epsilon A and t represent random errors of technical impact and obey independent normal distribution;

η _t ^L ＝1-(η ₀ +η ₁ ST _t +η ₂ ST _t ² )

ST _t ＝(1-δ _Z )ST _t-1 +Z _t

wherein δZ represents the contaminant inventory depreciation rate;

log q _t ＝ρ _q log q _t-1 +ε _q，t ，ε _q，t ～N(0，σ _q ² )

CE _t ＝-ΛμM _t [(1-er _t )ln(1-er _t )+er _t ]

Wherein Λ represents an enterprise carbon emission reduction cost coefficient;

P _t ^Z ＝Λln(1-er _t )

the following two variables are defined:

/>

1＝(1-ω)(P _t ^* ) ^1-ε +ωπ _t ^ε-1 。

6. a method of carbon emissions accounting for a campus of claim 5, wherein: the DSGE model solving method comprises the following steps: the steady state of three elements of capital stock, labor and energy are calculated as:

Z _ss ＝μM _ss

Z _ss ＝δ _Z ST _ss 。

7. an electronic device comprising a processor and a memory communicatively coupled to the processor for storing processor-executable instructions, characterized in that: the processor is configured to perform a campus carbon emission accounting method as set forth in any one of claims 1-6.

8. A server, characterized by: comprising at least one processor, and a memory communicatively coupled to the processor, the memory storing instructions executable by the at least one processor to cause the at least one processor to perform a campus carbon emission accounting method as claimed in any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, characterized in that: the computer program when executed by a processor implements a method of campus carbon emission accounting as claimed in any one of claims 1 to 6.