An Empirical Study on the Factors Influencing Carbon Emissions in Heilongjiang Province Based on VAR Model

: The time series data of Heilongjiang Province from 2000-2019 were selected to measure carbon emissions in Heilongjiang Province according to the IPCC calculation formula. On this basis, a vector autoregressive model between economic growth, industrial structure, energy intensity, energy structure and carbon emissions is established, and the EG test and impulse response function are used to conduct an empirical study. The results show that: there is a long-term equilibrium relationship between carbon emissions and economic scale, industrial structure, energy intensity and energy structure; the optimization of industrial structure and energy structure will suppress the increase of carbon emissions, while the growth of economic scale and energy intensity will promote the increase of carbon emissions.


Introduction
In his speech at the 75th General Debate of the United Nations General Assembly in September 2020, President Xi Jinping said that China strives to peak its carbon dioxide emissions by 2030 and to achieve the goal of carbon neutrality by 2060.
Heilongjiang Province is a major agricultural and industrial province in China, and has been developing comprehensively in multiple industries, but it has been adopting a sloppy and inefficient economic development method in the early stage of rapid economic development, paying a large environmental cost and keeping its carbon emissions high. Therefore, an empirical study on the factors influencing carbon emissions in Heilongjiang Province based on the measurement of carbon emissions in the past years can provide a basis for formulating a healthy economic development policy and an effective strategy to control carbon emissions in Heilongjiang Province.

Review of the literature
Regarding the measurement of carbon emissions, Zheng Yuan et al [1] . applied the life-cycle method to estimate the agricultural carbon emissions in Yunnan Province based on six levels of carbon sources in the agricultural production process: fertilizer, pesticide, diesel, agricultural film, agricultural irrigation, and agricultural tillage; Wang Shaojian et al. [2] combined the material balance method of carbon emissions and the structural decomposition model to measure the carbon emissions of each industry in 21 prefecture-level cities in Guangdong Province; Hu Jianbo et al. [3] used the non-competitive input-output model to measure the implied carbon emissions of each industrial sector in China based on the Chinese input-output table (extended table).
Study on the influencing factors of carbon emissions. Xuehua Zhang et al. [4] explored the drivers of energy carbon emissions in "2+26" cities by constructing spatial autocorrelation and LMDI models based on carbon emission data calculated from end-use energy consumption in "2+26" cities. Different from the above scholars, Li Zhiguo et al. [5] used the IPAT-LMDI method to identify the drivers of urban and rural household carbon emissions based on different structural models. Some other scholars have conducted empirical studies specifically on carbon emission drivers in Heilongjiang province [6][7][8] .

Research Methodology
This paper uses a vector autoregressive model (VAR) and an impulse response function based on the VAR model to empirically analyze the influencing factors of carbon emissions in Heilongjiang Province [9] in the general form shown in Equation (1): is a vector of endogenous variables; , ⋯ , and B are the coefficient matrices to be estimated; is a vector of exogenous variables; is a vector of perturbations; p is the model lag order; and T is the number of samples.

Variable Selection and data sources
To explore the influencing factors of carbon emissions in Heilongjiang province, four variables, namely GDP, industrial structure (IS), energy structure (ES) and energy intensity (EI), are selected in this paper. The data are obtained from the Heilongjiang Statistical Yearbook and the China Statistical Yearbook.
The data on carbon emissions in Heilongjiang province are measured according to the methodology recommended by the IPCC Guidelines for National Greenhouse Gas Emissions Inventories (2006 edition) [10] . This paper fully examines the characteristics of the three industries, integrates the carbon sources specific to each industry, and synthesizes the research results of several scholars to measure the carbon emissions in Heilongjiang Province from the following aspects, as shown in Figure  1.

Figure 1 Carbon emission measurement index system of Heilongjiang Province
The formula for measuring carbon emissions in this paper is as follows: ∑ ∑ * (2) (2) In the equation, . refer to the actual quantities of each type of carbon sources and their corresponding carbon emission coefficients, respectively.

Smoothing test
This paper uses time series data, so a smoothness test is required to avoid pseudo-regression. In this paper, ADF unit root test is chosen and the lag order is determined according to the minimization criterion of AIC. It is found that lnC, lnGDP, IS, EI, and ES all show non-stationarity at 10% significance level. Secondly, first-order differences were taken for them respectively, and the results rejected the original hypothesis as smooth variables at 10% significance level, i.e., all variables are first-order single integer, I(1),the VAR(2) model is established. In order to carry out the subsequent impulse response analysis, after the lag order is determined, the stability of the VAR also needs to be checked, that is, to determine whether its characteristic roots are all within the unit circle. As can be seen from Figure 2, the characteristic roots of VAR(2) are all distributed within the unit circle, which proves that VAR(2) is stable.

Co-integration test
EG cointegration test is used to find the long-run equilibrium relationship between carbon emissions and GDP, IS, EI and ES in Heilongjiang Province, and the long-run cointegration equation is: be seen that the energy structure has the least impact on carbon emissions, and the main reason for this according to the original data analysis is that the energy structure of Heilongjiang Province has not changed much over the years. Each percentage increase in industrial structure causes a decrease in carbon emissions by 1.0102 units. This is mainly because the optimization and adjustment of industrial structure makes the proportion of the secondary industry with high energy consumption decrease, which reduces the consumption of resources and greatly promotes the development of low carbon economy. In addition, carbon emissions are more sensitive to the effects of changes in industrial structure and economic scale.
The long-term cointegration model is error corrected to obtain the short-term fluctuation relationship, and according to the test results, the equations are significantly linearly correlated and the parameters pass the significance test, and the error correction model is shown in Equation (5) In the short term, the first-order lags of LNGDP, IS, EI, and ES all act in the same direction as the long-term results for LNC, and the last-period error has a larger adjustment for the current fluctuations in carbon emissions, with an adjustment of 0.9578 and a negative direction of adjustment.

Impulse Response Analysis
Using Eviews 11.0 to obtain the impulse response plots (Figure 3-figure 6).   As can be seen from the figure, when a positive shock to the economic scale is given, emissions are first affected positively and reach a peak in period 3, then begin to gradually weaken and basically maintain a weak negative state; when a positive shock to the industrial structure is given, it reaches a positive maximum in period 2, then reaches a negative shock maximum in period 4, and then the negative shock gradually weakens; when a positive shock to the energy intensity is given, carbon emissions reach a positive maximum in period 3, then the shock gradually oscillates and weakens and converges to zero; when a positive shock to the energy structure is given, two maximum positive effects are generated in periods 3 and 7, respectively.

Analysis of variance decomposition
On the basis of the impulse response function, the variance decomposition can further estimate the time lag and contribution size of the effect of variables. The results of the variance decomposition of carbon emissions in Heilongjiang province are shown in Figure 7.
As can be seen from Figure, the contribution of carbon emissions to itself is maintained at about 25%; the contribution of economic scale to carbon emissions is greater than that of carbon emissions itself from the fifth period, and its contribution begins to dominate, reaching a maximum contribution of about 37.47% in the seventh period and fluctuating between 30% and 35% afterwards; the contribution of industrial structure and energy structure to carbon emissions is smaller, but also in The contribution of industrial structure and energy structure to carbon emissions is smaller, but also increases period by period. The contribution rate of industrial structure is finally maintained at about 10%, and the contribution rate of energy structure is finally maintained at about 6%; the contribution rate of energy intensity to carbon emissions rapidly increases from 0.46% in the second period to 20.28% in the fourth period, and then slowly increases to about 26%, and the contribution rate of energy intensity exceeds the contribution rate of carbon emissions itself after the eighteenth period. It can be seen that economic scale makes the largest contribution to carbon emissions, followed by energy intensity, and finally industrial structure and energy structure. It shows that once the low carbon economic development is achieved and the obstacles encountered in the process of energy intensity improvement are solved so as to reduce the energy intensity, this will have a certain effect on the control role of carbon emissions in Heilongjiang Province.

Conclusions and Recommendations
(1) there is a long-term equilibrium relationship between GDP, IS, EI, ES and carbon emissions at the 5% significance level. The optimization of industrial structure and energy structure will suppress the increase of carbon emissions, while the growth of economic scale and energy intensity will promote the increase of carbon emissions. (2) the impact of industrial structure on carbon emissions changes from positive to negative, and carbon emissions will gradually decrease with the optimization of industrial structure; the impact of energy intensity on carbon emissions is weaker; energy structure has two waves of positive impact on carbon emissions and the peaks The peaks are similar. (3)The variance decomposition results show that the contribution of carbon emissions to itself is maintained at about 25%, and the contribution of economic scale to carbon emissions is the largest, followed by energy intensity, and finally industrial structure and energy structure. As a result, the following three recommendations are made for carbon emission reduction in Heilongjiang Province: (1) Change the way of economic growth;(2) Optimize industrial structure;(3) Transform the energy structure.