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Journal of Resources and Ecology >

2024 , Vol. 15 >Issue 4: 860 - 869

DOI: https://doi.org/10.5814/j.issn.1674-764x.2024.04.007

Impact of Human Activities on Ecosystem

Industrial Carbon Reduction Potential Measurement and Scenario Prediction in Shaanxi Province

  • WANG Wenjun ,
  • YING Xinru , * ,
  • KOU Chenlu
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  • School of International Business, Shaanxi Normal University, Xi’an 710119, China
* YING Xinru, E-mail:

WANG Wenjun, E-mail:

Received date: 2023-03-20

  Accepted date: 2023-09-25

  Online published: 2024-07-25

Supported by

The Shaanxi Social Science Federation Foundation Project(2021HZ1118)

The Shaanxi Normal University Graduate Student Innovation Team Project(TD2020006Y)

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Abstract

Promoting industrial carbon reduction is an inevitable step for achieving the Chinese carbon peak and neutrality targets. Based on the industrial energy consumption data of Shaanxi Province from 2011 to 2020, this study uses the IPCC calculation method to calculate the industrial carbon emissions in Shaanxi Province. The prediction model for industrial carbon emissions in Shaanxi Province was constructed based on the STIRPAT model from three aspects: population, economy, and technology. By setting three scenario models, the industrial carbon emissions from 2021 to 2035 and the time to achieve peak carbon neutrality were then predicted. The results show that the industry in Shaanxi Province cannot achieve a carbon peak under the baseline scenario, although it can achieve carbon peaking in 2030 under a low-carbon scenario or in 2025 under an enhanced low-carbon scenario. The predicted carbon peak values are 209.11 million t and 188.36 million t, respectively. Based on the results of this study, four policy recommendations are proposed: (1) strengthen publicity and education efforts to increase public participation in energy conservation and emission reduction; (2) promote the green transformation of industry and develop a green economy, including the active development of energy-saving and emission reduction technologies; (3) accelerate the implementation of industrial carbon reduction; and (4) promote the development and utilization of clean energy and increase efforts to adjust the energy structure.

Key words： industrial carbon reduction; potential measurement; STIRPAT model; scenario prediction

Cite this article

WANG Wenjun , YING Xinru , KOU Chenlu . Industrial Carbon Reduction Potential Measurement and Scenario Prediction in Shaanxi Province[J]. Journal of Resources and Ecology, 2024 , 15(4) : 860 -869 . DOI: 10.5814/j.issn.1674-764x.2024.04.007

1 Introduction

The rapid deterioration of the climate has become the most challenging and severe problem facing humanity. As one of the countries most vulnerable to climate change, China has implemented an active national strategy to address climate change. In 2020, the Chinese leader announced that China would strive to peak its carbon dioxide emissions by 2030 and work toward a carbon-neutral goal by 2060. In 2022, he once again mentioned the need to accelerate the green transformation of the development mode, and actively and steadily promote carbon peaking and carbon neutrality, as well as the planned and step-by-step implementation of carbon peaking actions.
In recent years, the research on carbon emissions has become increasingly extensive and in-depth. Scholars have conducted effective research around the estimation of industrial carbon emission reduction potential, as well as scenario design for carbon peak and carbon neutrality predictions and carbon reduction implementation paths. This research has focused mainly on three aspects. 1) The relationship between carbon emissions and industry development. Replacing traditional fossil fuels with electricity is the most effective way to achieve industrial development along with energy conservation and emission reduction, and low-carbon development will be conducive to high-quality economic development (Sun et al., 2017; Zhang et al., 2021; Yu and Wu, 2022). 2) Industrial carbon emission reduction projections under each designed scenario. Multiple carbon peak scenarios can be set up from different perspectives, and the changes in energy consumption and carbon emissions are analyzed to determine whether a carbon peak and carbon neutrality can be achieved (Bi and Wang, 2017; Ma and Chen, 2017; Wang et al., 2019). 3) The exploration of carbon reduction paths. Scholars have proposed emission reduction paths from the perspectives of electricity, agriculture, transportation, and the industries themselves. They believe that the integration of electricity innovation, green development in agriculture, the replacement of transportation power systems, and transformation of the industrial energy structure will accelerate the achievement of the “3060 target”①(① The “3060 target” aims to achieve peak carbon emissions by 2030 and carbon neutrality by 2060.) (Zhang et al., 2021; Lan, 2022; Shi and Ye, 2022; Zhao et al., 2022).
Many scholars have analyzed the relationship between industrial development and carbon emissions, as well as the impacts of population, economy, and technology on carbon emissions. Finally, some scholars have discussed the carbon emissions and peak time under different scenarios, as well as the potential implementation paths of carbon emission reduction. These studies have reference significance for continuously exploring the industry’s carbon emission reduction potential and achieving carbon peaking. However, as the results come from many different research perspectives and the actual situations of the chosen research objects, the conclusions on carbon reduction drawn for the various provinces and industries vary considerably. In the existing research, firstly, the focus on carbon reduction targets varies. Few studies use energy consumption in specific industries within the overall industrial sector as a starting point. Secondly, the parameter settings in scenario design have different focuses, which will result in very different final prediction results. Finally, there is limited summarization of feasible working policies for exploring the carbon reduction pathways.
Shaanxi Province was listed as one of the first low-carbon pilot provinces in 2010. According to the 13th Five Year Plan (2016-2020), efforts should be made to reduce the province’s carbon emission intensity by 45% from the 2005 baseline to 2020. In 2020, the energy consumption per 10000 yuan of GDP in the province decreased by 15% compared to 2015. The total energy consumption should be controlled within 139 million t of standard coal. By 2020, the proportion of coal in the total energy consumption should be reduced to about 70%, and the proportion of non-fossil fuels should reach 13%. Natural gas accounts for about 13% of the total primary energy consumption. According to the “14th Five Year Plan (2021-2025) for High Quality Development of Manufacturing Industry in Shaanxi Province”, the cumulative reduction in energy consumption per unit of industrial added value above a designated size will be 12% by 2025, and the carbon dioxide emissions per unit of industrial added value will be reduced by 16%. The issue of carbon emissions has received a great deal of attention from many countries and provinces, making it the top priority among environmental issues. Therefore, carbon reduction in industry is an inevitable trend and a necessary path for achieving the carbon peak and carbon neutrality.
To address these issues, this study calculates industrial carbon emissions based on energy data from the industries and their internal sub-sectors in Shaanxi Province. It sets three scenario parameters to predict the carbon peak time and corresponding carbon emissions under various policy and practical circumstances. At the end of the article, four policy recommendations are proposed. This article can provide feasible suggestions for helping the government to formulate and improve the industrial carbon emission reduction policies in Shaanxi Province, and it provides a reference for promoting economic development and environmental protection.

2 Calculation of industrial carbon reduction potential

2.1 Factor selection

Based on an extensive survey of relevant articles (Zhang, 2017; Wang and Wang, 2021), we used the STIRPAT model. In this model, the factors influencing industrial carbon emissions in Shaanxi Province are divided into three major aspects (Table 1): Population, economy, and technology. The population factor is represented by the total population of Shaanxi Province at the end of the year. Economic factors are expressed in terms of gross domestic product and per capita industrial value added. Technical factors are represented by industrial carbon emission intensity and industrial energy intensity.
Table 1 Industrial carbon emission forecasting model index system for Shaanxi Province
Model First level indicator Second level indicator Unit Symbol Properties
Industrial carbon emission projections Population Year-end population 104 persons P1 +
Economy GDP 108 yuan A1 +
Industrial value added per capita 104 yuan person-1 A2 +
Technology Industrial carbon emission intensity t CO2 (104 yuan)-1 T1 -
Industrial energy intensity t standard coal (104 yuan)-1 T2 -
The correlation coefficients between industrial carbon emissions in Shaanxi Province and five influential factors were calculated, namely, the number of permanent residents in Shaanxi Province at the end of the year, the gross domestic product of Shaanxi Province, the per capita value of industrial added value, the intensity of industrial carbon emissions, and the intensity of industrial energy, for the period of 2011‒2020 (Table 2). The results indicate significant positive correlations between the year-end population, GDP, and per capita industrial added value of Shaanxi Province and carbon emissions, while the intensity of industrial carbon emissions and industrial energy intensity have negative correlations with carbon emissions. The industrial carbon emissions and carbon emission intensity in Shaanxi Province showed a positive correlation between 2010 and 2015, but a negative correlation between 2015 and 2020. Therefore, the overall correlation between the two is relatively weak between 2010 and 2020 (Table 2).
Table 2 Correlation coefficients of industrial carbon emissions and various influencing factors for the time-period of 2010 to 2020
Influencing factors Year-end
population		 GDP Industrial value
added per capital		 Industrial carbon
emission intensity		 Industrial
energy intensity
Industrial carbon emissions 0.793 0.879 0.806 -0.005 -0.779

2.2 Data sources

The data for this study were all obtained from the “Shaanxi Statistical Yearbook” for each year from 2011 to 2020. The energy consumption data of industrial sub-sectors came from the energy consumption of industrial enterprises above a designated size and grouped by industry in the Shaanxi Statistical Yearbook. The gross domestic product and added value of industrial sub-sectors came from the main economic indicators of industrial enterprises above a designated size.
This study adopted the IPCC carbon emission factor method. The carbon emissions generated by seven types of energy consumption in six high energy consuming industries of 40 industrial sub-sectors in Shaanxi Province from 2011 to 2020 were calculated. Based on those calculations, the total amount, intensity, and structure of industrial carbon emissions in Shaanxi Province were analyzed.
The specific conversion coefficients provided by the IPCC guidelines are shown in Table 3.
$C=\underset{j}{\mathop \sum }\,\left( {{E}_{j}}\times {{K}_{j}} \right)$
Table 3 Energy conversion factors of standard coal
Types of energy Raw coal Coke Gas Crude oil Gasoline Kerosene Diesel
Discount factor for standard coal (kgce kg-1) 0.7143 0.9714 1.33 1.4286 1.4714 1.4714 1.4571
In Equation (1), C denotes total carbon emission; j denotes energy type; Ej denotes the j-th energy quantity in standard coal; and Kj denotes the carbon emission coefficient of the j-th energy type.

2.3 Model construction

(1) Extended STIRPAT model
The STIRPAT model is based on the idea that the impact of human activities on the ecological environment reflects the combined effects of population (P), wealth (A), and technology (T). This model is widely used in research on the decomposition of factors affecting the environment, such as energy consumption and a low-carbon economy. This study extended the original model in several ways. Firstly, the population factor was decomposed into the total population of Shaanxi Province at the end of the year P1. Furthermore, the economic factors were decomposed into Shaanxi Province’s Gross Domestic Product (A1) and Industrial Per Capita Value Added (A2). Finally, the technical factors were decomposed into industrial carbon emission intensity (T1) and industrial energy intensity (T2).
Based on the above indicator selection, the extended STIRPAT model is as follows.
$\begin{align} & \ln Y=\ln \alpha +{{\beta }_{1}}\ln {{P}_{1}}+{{\beta }_{2}}\ln {{A}_{1}}+{{\beta }_{3}}\ln {{A}_{2}}+ \\ & \ \ \ \ \ \ \ \ {{\beta }_{4}}\ln {{T}_{1}}+{{\beta }_{5}}\ln {{T}_{2}}+\text{ln}\varepsilon \\ \end{align}$
In the model of Equation (2), Y denotes industrial carbon emissions (104 CO2); P1 denotes total population at the end of the year (104 persons); A1 denotes gross product (108 yuan); A2 denotes industrial value added per capita (104 yuan person-1); T1 denotes industrial carbon emission intensity (t CO2 (104 yuan)-1); T2 denotes industrial energy intensity (t standard coal (104 yuan)-1); lnα is a constant term; β1-β5 is the regression coefficient; and lnε is the error term. The data for specific indicators are shown in Table 4.
Table 4 Specific data for the factors influencing industrial carbon emissions in Shaanxi Province in 2011-2020
Year lnY lnP1 lnA1 lnA2 lnT1 lnT2
2011 9.25 8.23 9.41 3.56 -0.15 -0.29
2012 9.41 8.23 9.56 3.67 -0.14 -0.36
2013 9.59 8.23 9.67 3.82 -0.08 -0.40
2014 9.65 8.24 9.76 3.90 -0.11 -0.44
2015 9.84 8.24 9.79 3.81 0.04 -0.42
2016 9.84 8.25 9.85 3.84 -0.02 -0.45
2017 9.85 8.25 9.97 3.98 -0.13 -0.54
2018 9.81 8.26 10.08 4.13 -0.28 -0.62
2019 9.84 8.26 10.16 4.12 -0.32 -0.65
2020 9.85 8.28 10.17 4.09 -0.32 -0.63
(2) Model testing
Using SPSS 26.0 software, the data were first subjected to ordinary least squares estimation to test whether the multiple linear regression could represent the validity of the model. The results (Table 5) show that the adjusted R2 is 0.98, the F-test statistic is 17562.966, and the P-value is less than 0.001, indicating that the model passed the overall significance test and the overall fitting effect is good. Then, based on the t-values, the results of testing the regression coefficients of each independent variable show that, except for lnA2, the P-values of the t-tests of the remaining independent variables are all less than 0.1, so they pass the test at the significance level of 10%. However, the VIF (variance inflation factor) values of all independent variables are much greater than 10, with the highest reaching 970.825, indicating the presence of very serious multicollinearity among the independent variables of the model. Therefore, the commonly used ordinary least squares estimation is not applicable to the data in this study. In order to obtain a reliable fitted model, the problem of multicollinearity among the independent variables must be resolved.
Table 5 Ordinary least squares estimation results
Variable Coefficient Standard error Standard coefficient t P VIF
$\ln \alpha $ -5.218 1.709 -3.053 0.055
$\ln {{P}_{1}}$ 0.875 0.243 0.049 3.602 0.037 15.958
$\ln {{A}_{1}}$ 0.774 0.081 0.848 9.500 0.002 699.187
$\ln {{A}_{2}}$ 0.006 0.025 0.005 0.224 0.837 41.032
$\ln {{T}_{1}}$ 1.168 0.041 0.601 28.702 <0.001 38.456
$\ln {{T}_{2}}$ -0.500 0.195 -0.269 -2.557 0.083 970.825

Note: Adjusted R2=1, F-statistic=17562.966, P (F-statistic) < 0.001.

(3) Ridge regression
Ridge regression is a biased estimation regression method that uses an improved ordinary least squares method to handle multicollinearity problems with independent variables. Ridge regression abandons the unbiased estimation of the ordinary least squares method and loses some information. The estimated partial regression coefficients are often closer to the real situation, thereby improving the stability and reliability of the regression model. In ridge regression, k represents the ridge parameter, and its value range is 0-1. When k=0, ridge regression is a least squares estimation. A larger k indicates that the ridge regression has lost more information, so the value of k should be as small as possible. The k value can be determined by using the ridge plot. The principle for selecting k values is based on the minimum k value when the standardized regression coefficients of each independent variable tend to stabilize. The R2-k graph reflects the R2 of the ridge regression with a certain k-value.
In a ridge plot, the horizontal axis represents the ridge parameters, and the vertical axis represents the changes in the normalized ridge regression coefficients for each variable. The R2-k graph reflects the goodness of fit of the ridge regression with a certain k-value. Thus, the horizontal axis represents the ridge parameter, and the vertical axis represents the goodness of fit.
According to Fig. 1 and Fig. 2, the change gradually stabilizes at k=0.2, with R2 reaching 0.9. At that point, another ridge regression was performed with stop=0.2. As shown in Fig. 3, the performance is relatively stable around k=0.05, and the R2 value in Fig. 4 can reach 0.995. Therefore, the ridge regression fitting result at k=0.05 was selected.
Fig. 1 Ridge trace plot, where the range of k is 0-1, the horizontal scale is 0.2, and the vertical scale is 0.2
Fig. 2 R2-k diagram, where the range of k is 0-1, the horizontal scale is 0.2, and the vertical scale is 0.05
Fig. 3 Ridge trace plot, where the range of k is 0-0.2, the horizontal scale and the vertical scale both are 0.05
Fig. 4 R2-k diagram, where the range of k is 0-0.2, the horizontal scale is 0.05, and the vertical scale is 0.005
In the two ridge regression tests (Figs. 1-4), the k value is determined by the stable trend of the ridge plot. In Fig. 1, the normalized regression coefficient tends to stabilize at a k value of 0.2. Therefore, taking k=0.2, the ridge plot in Fig. 3 shows that the standardized regression coefficient is more stable at k=0.05, and the R2 value in Fig. 4 can reach 0.995. Therefore, the ridge regression fitting results at k=0.05 were used.
The ridge regression equation at k=0.05 was then tested, and the results are shown in Table 6. According to the test results, the P-values of the t-test for each independent variable are all less than 0.1, passing the test at the 10% significance level. This indicates that each independent variable has a strong explanatory power towards the independent variable. The determination coefficient R2 is 0.9948, indicating that the explanatory power of the independent variable on the dependent variable is as high as 99.48%. The F-statistic is 116.558, and P is 0.0012, indicating that the overall fitting effect of the model is very good and that the test is passed at the 1% significance level.
Table 6 Ridge regression results
Variable Coefficient Standard
error		 Standard
coefficient		 t P
lnP1 3.629 1.345 0.202 2.698 0.074
lnA1 0.414 0.044 0.454 9.418 0.002
lnA2 0.265 0.084 0.227 3.155 0.051
lnT1 1.148 0.088 0.590 13.096 <0.001
lnT2 -0.511 0.074 -0.275 -6.898 0.006
Constant -25.412 10.796 <0.001 -2.354 0.099

Note: R2=0.9948, F-statistic=116.558, P (F-statistic)=0.0012.

The fitting value was calculated based on the model fitted by the ridge regression. Comparing the fitted values with the actual values (Fig. 5 and Table 7), the errors between them are all less than 5%. This indicates that the overall fitting effect is very good, so the model can better explain the relationship between industrial carbon emissions and the various influencing factors in Shaanxi Province. Therefore, the specific form of the model can be determined as the ridge regression equation in equation (3).
$\begin{align} & \ln Y=-25.412+3.629\ln {{P}_{1}}+0.414\ln {{A}_{1}}+ \\ & \ \ \ \ \ \ \ \ 0.265\ln {{A}_{2}}+1.148\ln {{T}_{1}}-0.511\ln {{T}_{2}} \\ \end{align}$
Fig. 5 Fitting of the actual and predicted values of industrial carbon emissions from 2011 to 2020
Table 7 Model predictions fitted to the actual values
Year Industrial carbon emissions (×104 t)
2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
Measured value 10445.27 12261.13 14655.74 15556.25 18684.84 18683.48 18882.13 18157.55 18788.96 19000.00
Predicted value 10517.87 12197.73 14852.77 15623.46 18407.47 18272.80 18822.90 18492.77 18626.25 19208.81
Percentage of error 0.70% -0.52% 1.34% 0.43% -1.48% -2.20% -0.31% 1.85% -0.87% 1.10%
In the model of equation (3), Y denotes industrial carbon emissions (104 CO2); P1 denotes total population at the end of the year (104 persons); A1 denotes gross product (108 yuan); A2 denotes industrial value added per capita (104 yuan person-1); T1 denotes industrial carbon emission intensity (t CO2 (104 yuan)-1); and T2 denotes industrial energy intensity (t standard coal (104 yuan)-1).

2.4 Analysis of the results

The ridge regression results and fitting results show that the model had very good fitting results from 2011 to 2015, with errors of less than 1%. However, the fitting effect of the model was relatively poor after 2016, although it could keep the errors to within 5%. Therefore, the predictive model can be considered to have a good predictive ability. The prediction model for industrial carbon emissions in Shaanxi Province is given in equation (4).
$Y={{\text{e}}^{\left( -25.412+3.629\ln {{P}_{1}}+0.414\ln {{A}_{1}}+0.265\ln {{A}_{2}}+1.148\ln {{T}_{1}}-0.511\ln {{T}_{2}} \right)}}$
In the model of equation (4), Y denotes industrial carbon emissions (104 CO2); P1 denotes total population at the end of the year (104 persons); A1 denotes gross product (108 yuan); A2 denotes industrial value added per capita (104 yuan person-1); T1 denotes industrial carbon emission intensity (t CO2 (104 yuan)-1); and T2 denotes industrial energy intensity (t standard coal (104 yuan)-1).
From the coefficient perspective, the increases in population factors, economic factors, and carbon emission intensity will accelerate the increase in industrial carbon emissions in Shaanxi Province. Population factors and carbon emission intensity both have significant impacts on industrial carbon emissions in Shaanxi Province. For every 1% increase in population and carbon emission intensity, carbon emissions will increase by 3.629% and 1.148%, respectively. Gross domestic product and per capita industrial added value increases of 1% will cause carbon emissions to increase by 0.414% and 0.265%, respectively. For every 1% increase in industrial energy intensity, carbon emissions will decrease by 0.511%. The intensity of industrial carbon emissions is a restraining factor for industrial carbon emissions, and its coefficient in this article is inconsistent with most of the other literature. This inconsistency is mainly because the intensity of industrial carbon emissions showed a significant upward trend from 2011 to 2015, but it then showed a downward trend after 2015. In the early stage, the growth rate of carbon emissions was faster than that of the GDP. After 2015, carbon emissions gradually stabilized, but GDP still maintained a relatively stable growth. The positive correlation effect with carbon emissions in the early stage was higher than the negative correlation effect in the later stage. This difference results in a positive correlation between the intensity of industrial carbon emissions and carbon emissions.

3 Industrial carbon reduction scenario projections

3.1 Scenario design

Based on the current policy objectives and environmental constraints, the parameters of the indicators in the forecasting model were set and then used to forecast carbon emissions. Combining the development goals and climate objectives of the country as a whole and Shaanxi Province, the scenarios were designed with different objectives for industrial carbon emissions in Shaanxi Province, and three scenarios were set up: a baseline scenario, a low-carbon scenario, and an enhanced low-carbon scenario, as well as the corresponding economic parameters.
(1) Baseline scenario
This scenario keeps the existing carbon reduction policies unchanged, with the goal of maintaining the status quo while still pursuing high-speed GDP growth. It includes the continuation of the current industrial production scale, industrial structure, technological level, energy consumption structure and intensity model, without providing additional protection for the environment or reducing carbon and pollution. It maintains constant or minor changes based on the changing rates of existing indicators.
(2) Low-carbon scenario
Taking the “14th Five Year Plan (2021-2025) for High Quality Development of Manufacturing Industry in Shaanxi Province” as the goal, by 2025, the cumulative reduction in energy consumption per unit of industrial added value above a designated size will be 12%. This scenario reduces carbon dioxide emissions per unit of industrial added value by 16%, and makes additional efforts to reduce the industrial carbon intensity. In this scenario, the GDP growth rate has slowed down. The government has introduced relevant policies and measures to guide the public and enterprises in energy conservation and emission reduction. The industrial sector places greater emphasis on production quality and improves energy system efficiency. The total consumption of fossil fuels remains at a reasonable level.
(3) Enhanced low-carbon scenario
The goal of this scenario is to exceed the target tasks of the 14th Five Year Plan (2021-2025). In this scenario, GDP maintains a relatively stable growth rate, and unconventional emission reduction policies and measures are adopted. The efficiency of the energy system has significantly improved. The total energy consumption remains at a relatively low level. Various industrial departments have significantly improved in terms of production scale, industrial structure, and technological level. The government's efforts in environmental protection are stronger.

3.2 Scenario parameter setting

The specific scenario parameters include the following five aspects (Table 8).
Table 8 Parameter settings for the three scenarios
Scenario Period Annual average growth rate (%)
P1 A1 A2 T1 T2
Baseline scenario 2021-2025 0.5 9 5 -5 -5
2026-2030 0.4 11 8 -7 -5.5
2031-2035 0.3 12 11 -9 -6.5
Low-carbon scenario 2021-2025 0.4 8 4 -6 -6
2026-2030 0.2 10 7 -8 -6.5
2031-2035 0 11 10 -10 -7
Enhanced
low-carbon scenario		 2021-2025 0.3 7 3 -7 -7
2026-2030 0 9.5 6 -9 -7.5
2031-2035 -0.1 10 9 -11 -8
(1) Size of resident population (P1). According to the data from the Seventh National People’s Congress, the growth rate of the permanent population in Shaanxi Province from 2010 to 2020 was relatively slow. The number of people over the past decade increased by 1.33 million. The growth rate was only 3.55%, and the average annual growth rate was 0.44%. Based on the changes in population numbers in various cities throughout the province, during the two population censuses, only Xi’an achieved net population inflows, while the other cities experienced net population outflows. The growth of the permanent population in Shaanxi Province has been relatively stable. Currently, China’s fertility rate is growing slowly, with weak population growth and regional mobility. The fluctuations in the number of permanent residents in Shaanxi Province are not obvious. There may even be negative population growth in the future. Jiangsu Province experienced a historically negative natural population growth rate in 2021. Therefore, the average annual growth rates of the permanent population in Shaanxi Province from 2021 to 2025 were set at 0.5%, 0.4%, and 0.3%; the average annual growth rates from 2026 to 2030 were set at 0.4%, 0.2%, and 0.00%; and the average annual growth rates from 2031 to 2035 were set at 0.3%, 0.00%, and -0.1%.
(2) GDP (A1). The “14th Five Year Plan (2021-2025) for National Economic and Social Development of Shaanxi Province and the Outline of the 2035 Long Range Goals,” released in 2021mentions that by 2035, the regional GDP will reach 3.6 trillion yuan. This means that compared to 2019, the GDP growth rate will reach nearly 40%, so before 2035, Shaanxi Province still needs to continuously expand its economy. Therefore, the annual average growth rates of Shaanxi Province’s gross domestic product in the three scenarios from 2021 to 2025 are set to 9%, 8%, and 7%; the annual average growth rates from 2026 to 2030 are set as 11%, 10%, and 9.5%; and the annual average growth rates from 2031 to 2035 are set as 12%, 11%, and 10%.
(3) Industrial value added per capita (A2). According to the “14th Five Year Plan (2021-2025) for High Quality Development of Manufacturing Industry in Shaanxi Province” formulated by the Shaanxi Provincial Government, during the “14th Five Year Plan” period, the average annual growth rate of manufacturing added value will reach over 7%. By 2025, the proportion of manufacturing added value to regional GDP will reach 23%. The average annual growth rate of per capita industrial added value from 2011 to 2019 was 7.19%. In 2019, the growth rate of industrial per capita added value was only -0.83%. Therefore, the average annual growth rates for the three scenarios of industrial per capita added value from 2021 to 2025 were set as 5%, 4%, and 3%; the annual average growth rates from 2026 to 2030 were set as 8%, 7%, and 6%; and the annual average growth rates from 2031 to 2035 were set as 11%, 10%, and 9%
(4) Industrial carbon emission intensity (T1). The “14th Five Year Plan (2021-2025) for High Quality Development of Manufacturing Industry in Shaanxi Province” mentions that by 2025, the carbon dioxide emissions per unit of industrial added value will be reduced by 16%. The average annual growth rate over the past five years should reach -3.43%. The average annual increase in industrial carbon dioxide emissions intensity of Shaanxi Province’s gross domestic product from 2011 to 2020 was -2.02%. In recent years, the intensity of industrial carbon emissions in Shaanxi Province has decreased significantly compared to before 2015. The declining rate of industrial carbon emissions intensity in 2019 was only 3.95%. Therefore, the annual growth rates of industrial carbon dioxide emissions intensity for the three scenarios of gross domestic product from 2021 to 2025 were set as -5%, -6%, and -7%; and the annual growth rates for the years 2026 to 2030 were set as -7%, -8%, and -9%. As technological advancements become increasingly difficult to break through over time, the fluctuation ranges of the annual growth rates from 2031 to 2035 were set as -9%, -10%, and -11%.
(5) Industrial energy intensity (T2). The “14th Five Year Plan (2021-2025) for the High Quality Development of the Manufacturing Industry in Shaanxi Province” clearly targets a cumulative reduction of 12% in energy consumption per unit of industrial added value above a designated size by 2025. The average annual growth rate of energy consumption per unit of gross domestic product in Shaanxi Province from 2011 to 2020 was -4.40%. The average growth rate of energy consumption in Shaanxi Province from 2015 to 2019 was 3.74%. The average annual growth rate of GDP from 2015 to 2019 was 9.84%. Because the improvements in energy consumption technology are relatively slow, the average annual growth rates in the three scenarios of GDP energy intensity from 2021 to 2025 were set as -5%, -6%, -7%; and the average annual growth rates from 2026 to 2030 were set as -5.5%, -6.5%, and -7.5%. Due to bottlenecks in the mid-to-late stage of technological advancement, the average annual growth rate decreases from 2031 to 2035 were set as -6.5%, -7.0%, and -8.0%.

3.3 Development projections under the different scenarios

Based on the above settings for the different parameters of the three scenarios, the projections of industrial carbon emissions in Shaanxi Province for 2021-2035 were derived (Table 9). The trend diagram was drawn, and is shown in Fig. 6.
Table 9 Forecast of industrial carbon emissions from 2021- 2035 (unit: 104 t)
Year Baseline
scenario		 Low-carbon
scenario		 Enhanced
low-carbon scenario
2021 19002.72 18883.53 18656.36
2022 19452.83 19215.42 18721.33
2023 19987.36 19573.44 18784.99
2024 20588.86 19742.59 18808.67
2025 21264.77 20079.34 18836.80
2026 21789.51 20279.39 18813.27
2027 22352.92 20434.88 18797.20
2028 23016.85 20596.09 18705.90
2029 23662.72 20774.12 18535.51
2030 24248.82 20911.24 18359.35
2031 24820.24 20873.42 18068.73
2032 25475.81 20713.28 17673.90
2033 26147.06 20443.01 17177.69
2034 26829.35 19958.36 16538.14
2035 27233.85 19790.82 15786.55
Fig. 6 Trends in Industrial carbon emissions from 2021 to 2035 under the three scenarios
Figure 6 show that there is no turning point under the baseline scenario, and the carbon emissions show significant growth. Carbon peaking cannot be achieved before 2030. Under the low-carbon scenario, the industrial carbon emissions in Shaanxi Province show a turning point in 2030, and the carbon emissions slowly decrease after 2030. Under the enhanced low-carbon scenario, the industrial carbon emissions in Shaanxi Province show a turning point in 2025, and then a significant downward trend. Before the inflection points appeared, carbon emissions in all three scenarios increased, with the baseline and low-carbon scenarios showing faster growth rates, while the enhanced low-carbon scenario shows slower growth. After the inflection point appeared, the reduction in the rate of carbon emissions in the low-carbon scenario is slower, while the reduction in the enhanced low-carbon scenario is faster.
Under the baseline scenario, the predicted industrial carbon emissions in Shaanxi Province in 2021 are 190.2 million t. The predicted value for 2035 is 272.33 million t. The growth rate in 2035 compared to 2021 is 43.3%, and there is no turning point. If Shaanxi Province continues to adopt the current energy-saving and emission reduction policies and measures, achieving carbon emissions peaking in the future will be difficult, and there may even be the possibility of a drastic increase in emissions.
Under the low-carbon scenario, the predicted value for 2021 is 188.83 million t and the predicted value for 2035 is 197.9 million t. The growth rate in 2035 compared to 2021 is 4.8%. In the low-carbon scenario, there is a turning point in 2030, and carbon emissions continue to decline thereafter. In this scenario, carbon peaking can be achieved effectively, but the actual carbon reduction is relatively low. The predicted peak carbon emissions in 2030 is 20911 million t, which is still higher than the actual industrial carbon emissions were in 2019.
Under the enhanced low-carbon scenario, the predicted carbon emissions for 2021 are 186.56 million t. The predicted value for 2035 is 157.86 million t, for a growth rate of -15.38%. The peak appears in 2025, with predicted carbon emissions of 188.36 million t, which is lower than the actual carbon emissions were in 2019. The carbon reduction rate is relatively slow between 2026 and 2031, and there is a significant decrease after 2031. This is consistent with the goal setting of “3060”. After the inflection point of carbon emissions occurs, there will not be a sharp decline immediately, but a plateau period with relatively small fluctuations, followed by a significant decrease after the transition.
In recent years, the industrial carbon emissions in Shaanxi Province have stabilized. From 2015 to 2019, the actual carbon emissions fluctuated between 1.90×108 t and 1.80×108 t, with relatively small fluctuations. The predicted carbon emissions under the three scenario settings can reflect the following considerations. Under the baseline scenario, carbon emissions show a linear growth trend, reaching 2.72×108 t by 2035. Compared to 2019, this represents an increase of 45%. Under the low-carbon and enhanced low-carbon scenarios, the fluctuations in carbon emissions are relatively small. Taking 2030 as an example, the predicted carbon emissions for the three scenarios are 2.42×108 t, 2.09×108 t, and 1.84×108 t, respectively. The difference between the baseline scenario and low-carbon is 0.33×108 t, while the difference between the baseline scenario and enhanced low-carbon is 0.59×108 t, and the difference between low-carbon and enhanced low-carbon is only 0.26×108 t. However, by 2035, the difference between the low-carbon and enhanced low-carbon predicted values has significantly increased, to 0.40×108 t. Therefore, implementing stricter emission reduction measures can indeed reduce the future industrial carbon emissions of Shaanxi Province to a certain extent. Especially in the context of strengthening the low-carbon scenarios, it can achieve a carbon peak and the carbon neutrality goals very well.

4 Discussion

Based on the results of this study, several recommendations can be put forward.
(1) Strengthen publicity and education efforts to increase public participation in energy conservation and emission reduction. Human activities are the main source of carbon emissions, and only by subjectively establishing carbon reduction awareness can carbon emissions be effectively controlled. Firstly, the government plays an exemplary and leading role in achieving the energy-saving and carbon reduction goals and the work of its own people. Enterprises consciously practice new development concepts and low-carbon production methods, and they should be encouraged to actively participate in carbon reduction actions. The power of non-governmental environmental protection organizations should be fully mobilized, so they can play their role in advising and supervising, and promote more scientific and rational government decision-making on energy conservation and emission reduction issues. The participation of all people in energy conservation and emission reduction work should be vigorously enhanced, to promote and educate through various platforms such as the internet and media.
(2) Promote the green transformation of industry and develop a green economy. Economic development is a prerequisite for implementing various carbon reduction measures. Therefore, it is impossible to abandon economic growth in order to achieve carbon reduction. To achieve a win-win situation between economic growth and carbon emissions reduction, industrial development and transformation must be green oriented. The developed light industry in Shaanxi Province can be utilized to transform industrial development from energy intensive to technology intensive. Shaanxi Province can rely on its rich natural and historical tourism and cultural industry advantages to vigorously develop the tourism and cultural service industry. The convenient regional and transportation advantages can be utilized to continuously increase the proportion of the tertiary industry and increase the contribution of the service industry to the economy.
(3) Actively develop energy-saving and emission reduction technologies and accelerate the implementation of industrial carbon reduction. Firstly, relying on large-scale new energy enterprises such as LONGI Green Energy and BYD in Shaanxi Province, we will increase investment in research and development funds. We will also accelerate research on the methods for energy conservation, emission reduction, and new energy use, and achieve the implementation of energy-saving and emission reduction technologies. Secondly, the government and enterprises should take advantage of the many engineering universities and research institutes in Shaanxi to promote the implementation of a process mechanism that combines industry, academia and research. Eventually this effort will realize the research and development and innovation of industrial energy-saving and emission reduction technologies.
(4) Promote the development and utilization of clean energy, and increase efforts to adjust the energy structure. Shaanxi Province’s own resource advantages have led to a long-term dependence on the consumption of fossil fuels such as coal for economic development. Shaanxi Province now has relatively limited utilization of hydropower and wind resources. In the future, we should increase the utilization of clean energy sources and adjust the energy structure. Based on the regional characteristics and existing conditions of Shaanxi Province, wind power generation should be developed in northern Shaanxi, solar energy resource utilization should be improved in the Guan zhong region, and the hydropower industry should be vigorously developed in southern Shaanxi. Efforts should also be made to further optimize the energy consumption structure in Shaanxi Province and make it more reasonable and perfect.

5 Conclusions

This study calculated the industrial carbon emissions in Shaanxi Province and selected five indicators from the three aspects of population, economy, and technology. The simulations predicted the industrial carbon emissions and the time to achieve a carbon peak in Shaanxi Province from 2021 to 2035 under three scenarios. These analyses have led to three main conclusions.
(1) The industrial carbon emissions in Shaanxi Province showed a steady upward trend from 2011 to 2020, with an average annual growth rate of 6.2%.
(2) Population and industrial carbon emission intensity are the main factors influencing industrial carbon emissions in Shaanxi Province. A 1% increase in population and industrial carbon emission intensity will lead to a 3.629% and 1.148% increase in industrial carbon emissions, respectively.
(3) The baseline scenario cannot achieve carbon peaking. The low-carbon scenario will achieve carbon peaking around 2030, while the enhanced low-carbon scenario will achieve carbon peaking by 2025. Therefore, Shaanxi Province must implement stricter carbon reduction measures for its industries.

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