Driving Factors and Peak Forecasting of Carbon Emissions from Public Buildings Based on LMDI-SD

Wang, Yongkun; Liang, Yang; Shao, Liangshan

doi:https://doi.org/10.1155/2022/4958660

Discrete Dynamics in Nature and Society

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Abstract Introduction Methods Conclusions Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2022 | Article ID 4958660 | https://doi.org/10.1155/2022/4958660

Driving Factors and Peak Forecasting of Carbon Emissions from Public Buildings Based on LMDI-SD

Yongkun Wang,¹Yang Liang,¹and Liangshan Shao¹

Academic Editor: Polinpapilinho Katina

Received18 Jan 2022

Accepted16 Apr 2022

Published27 Apr 2022

Abstract

Public buildings, with the increasing level of energy consumption, are a key area of energy conservation and emission reduction in China’s construction industry. In order to reduce carbon emissions of public buildings, realize the targets of energy conservation and emission reduction, the LMDI method is used to construct China’s public building macrocarbon factor decomposition model, identify the main driving factors affecting the change of carbon emissions from public buildings, and build a system dynamics model based on the decomposition results to predict China’s public building carbon emissions peak. The results show that reducing carbon emission coefficient and reducing energy intensity are the main driving factors to restrain the growth of carbon emission from public buildings. Under the base scenario, carbon emissions from public buildings will peak at 1.242 billion tons in 2041. Under the comprehensive regulation of energy structure, economic growth rate, investment level of scientific research and education, and carbon sink capacity, the carbon emissions of public buildings will reach the peak in 2030, and the adjustment of energy structure has a significant impact on the peak and peak time of carbon emissions of public buildings.

1. Introduction

Sustainable development and global warming are still the main issues of social development today. The “China Building Energy Research Report 2020” shows that in 2018, the national building operation stage carbon emissions were 2.11 billion tons, accounting for 42.8% of the building’s life cycle carbon emissions. Among them, the carbon emissions during the operation phase of public buildings were 784 million tons, accounting for 37.1% of the total during the operation phase of the buildings. Public buildings refer to civil buildings other than residential buildings, including commercial buildings, science, education, cultural and health buildings, and office buildings. [1] Large-scale public buildings generally have an energy-saving potential of more than 30% during the operation stage [2], and the potential for emission reduction is huge [3]. Therefore, identifying the influencing factors of carbon emissions during the operation phase of public buildings and further predicting the total amount of carbon emissions play an important role in achieving the target emission reduction tasks in China.

The existing research results on the carbon emissions of public buildings mainly focus on the carbon emission accounting [4, 5] and transactions [6, 7] of specific areas [8, 9] and specific building units [10, 11]. From a macroperspective, there are few studies on the analysis of the influencing factors, peak prediction, and energy-saving potential analysis of the overall carbon emissions of Chinese public buildings. The carbon emission system is a huge and complex system involving many fields such as society, economy, energy, technology, and environment [12]. After Forrester proposed system dynamics (SD) in 1958, the model became a powerful tool for studying complex system problems. Many scholars [13–16] used the SD model to analyze and predict carbon emissions and propose emission reduction measures. However, in some studies, the main driving factors of the changes in the carbon emissions of each subsystem in the SD model have not been identified and analyzed, and no scenario plans have been set up for the carbon emission driving factors. In order to solve these problems, this study uses the Logarithmic Mean Divisia index (LMDI) decomposition method [17] combined with the SD model to identify the main driving factors of the change in carbon emissions of public buildings. The LMDI method is based on the Kaya identity [18] and performs Divisia exponential decomposition. It has the advantages of no residuals and ease of use [8], and it has great flexibility in identifying driving factors.

Therefore, this study takes the public building operation stage as the research scope, uses the LMDI method to identify the various influencing factors and contribution rates of public building carbon emissions, and then builds an SD model from a system perspective to predict the peak carbon emissions and emission reduction potential of public buildings, proposing emission reduction measures, in order to provide ideas for the energy saving and emission reduction of public buildings in China.

2. Methods and Models

2.1. LMDI Factor Decomposition Model of Carbon Emission from Public Buildings

The LMDI method can be used to decompose the research object and quantitatively identify its influencing factors [19]. Since the production activities of the tertiary industry generally occur in the operation stage of public buildings [20], based on the original carbon emission factors, energy consumption, population, and Gross Domestic Product (GDP), considering the public building area, urbanization rate and the output value of the tertiary industry are included in the carbon emission factor decomposition formula. Accordingly, the Kaya identity is extended to establish the calculation formula of total carbon emission of public buildings, as shown in formula (1). Each variable is described in Table 1.

Here, is defined as energy intensity; the industrial structure is represented by . is economic density; is per capita area; the urbanization rate is represented by ; is population size; is the comprehensive coefficient of building carbon emission. The decomposition model of carbon emission factors of public buildings can be rewritten as follows:

The change of carbon emission of public buildings can be expressed as the sum of contribution values of all driving factors:where and represent the carbon emissions of public buildings at the end and beginning of the period, respectively.

Using the LMDI method, the difference in carbon emissions can be completely decomposed under addition. The decomposition formula of the carbon emission coefficient is shown in formula (4), and the decomposition formula of other factors only needs to replace the corresponding variables, so we will not repeat it here:

Here, , , , , , , and respectively, indicate the contribution of each driving factor to the change in carbon emissions of public buildings. If the factor decomposition result is greater than 0, it suggests that the factors of public buildings stimulate the increase of carbon emissions; on the contrary, if the decomposition results in less than 0, it has an inhibitory effect on the increase in carbon emissions and the greater the absolute value the higher the influence degree.

By constructing the LMDI factor decomposition model, this study identifies the main driving factors of public building carbon emissions, provides a basic framework for the construction of the system dynamics model, and provides the main control factors for the set of scenarios.

2.2. SD Model of Carbon Emission from Public Buildings

The SD model analyzes the structure of the complex system dynamically and studies the internal feedback relationships of the system. In addition to the seven factors decomposed by the LMDI method, this paper takes into account the ability of forest carbon sinks to “absorb and remove carbon” [7, 21] and considers carbon sinks as another factor affecting the total carbon emissions of public buildings. Based on this, the SD model is constructed to simulate the peak path of the total carbon emissions of public buildings.

3. The Data Source

This review studies the carbon emission of public buildings in China from 2010 to 2019. Among them, the total population, urban population, gross domestic product, tertiary industry output value, public building area, R&D expenditure, and forest area are data from the “China Statistical Yearbook (2010–2021), public building energy consumption data comes from “China Energy Statistical Yearbook” (2010–2021), fiscal revenue data comes from “China Fiscal Yearbook” (2010–2021), the comprehensive emission coefficient comes from the “China Building Energy Research Report” (2010–2020), and the relevant data sources required for model construction are authentic and reliable.

4. Analysis of LMDI Decomposition Results

According to the LMDI decomposition formula, combined with the data from 2010 to 2019, the decomposition results of the various factors of the carbon emissions of public buildings during the study period are calculated, as shown in Table 2. The contribution rate of each driving factor to the carbon emissions of public buildings is shown in Figure 1.

Table 2 reflects the breakdown of the driving factors of public buildings during the study period. On the whole, the carbon emissions of public buildings increased by 279 million tons from 2010 to 2019. Except for the slight decline in the growth of carbon emissions from 2013 to 2014, the carbon emissions of other years all increased to varying degrees, showing the “up-down-up” growth trend. From a time series point of view, the largest increase in carbon emissions from public buildings from 2010 to 2013 may be due to the fact that China is in the early stage of the “Eleventh Five-Year Plan” period, pursuing rapid growth in the tertiary industry, ignoring the coordinated development of the environment and economy to a certain extent, and causing excessive energy consumption and carbon emissions [22]. This can also be confirmed by the decomposition results of energy intensity and economic density. The area of land for public buildings is expanding rapidly, and economic activities are intensified. Economic growth at the cost of a large amount of energy consumption has gradually increased the economic density of land and carbon emissions. After 2012, due to the use of renewable energy and the implementation of energy-saving renovations, the carbon emission coefficient was greatly reduced. From 2013 to 2014, the carbon emissions of public buildings were reduced by 18 million tons compared with the previous year. Building energy-saving work has achieved remarkable results.

Figure 1 shows the contribution rates of various drivers of public building carbon emissions from 2010 to 2019.(1)Energy intensity and carbon emission coefficient are the negative driving factors that affect the carbon emission of public buildings. Among them, energy intensity reflects the relationship between economic growth and the operating energy consumption of public buildings and is the most important factor leading to the reduction of carbon emissions in public buildings and contributes the most to the suppression of carbon emissions in public buildings. Energy intensity caused a cumulative reduction of 131 million tons of carbon emissions from public buildings during 2010–2019, with a cumulative negative driving contribution rate of 47%. The increase in energy intensity during 2010–2012 led to an excessively rapid increase in carbon emissions. The cumulative contribution rate of the negative driving effect of the carbon emission coefficient was 33%, which is another major factor in reducing the carbon emission of public buildings. Since the carbon emission coefficient of fossil energy is basically unchanged, the main reason for the change in the carbon emission coefficient is the carbon emission coefficient of electricity. In recent years, under the optimization of energy structure and the application of emerging gas-power technology, public buildings have gradually begun to use clean energy to replace thermal power generation during operation. The carbon emission coefficient has been decreasing year by year, and its negative driving effect has become more and more obvious.(2)Among the positive drivers of carbon emissions, the per capita area and urbanization rate caused a cumulative increase in public building carbon emissions of 246 million tons and 158 million tons, respectively, and the positive driving contribution rates were 88% and 57%, respectively; while the positive driving contribution rate of the population size and industrial structure and economic density to carbon emissions is relatively small, and the three together account for 36%. From a vertical perspective, the annual growth rate of urbanization rate, population size, and industrial structure tend to be stable. Although the economic density has a larger trend of changes, the cumulative impact is minimal. In theory, if we want to reduce carbon emissions from public buildings, we need to reduce the per capita area, urbanization rate, and population size. However, in actual production and life, the activities of the tertiary industry mostly occur in public buildings. Reducing the area of public buildings will affect the development of the tertiary industry [20], and reducing the population size and slowing down the urbanization process are not in line with China’s current policies. Therefore, this article does not consider population size, urbanization rate, and public building area as adjustable factors.

In summary, energy intensity and carbon emission coefficients play a major role in driving changes in carbon emissions from public buildings. In the subsequent setting of the SD model scenario, adjustments will be made to reduce carbon emission coefficients and energy intensity and propose energy-saving and emission-reduction measures for public buildings based on this.

5. SD Peak Prediction of Carbon Emission from Public Buildings

5.1. SD Model Construction of Carbon Emission from Public Buildings

System dynamics studies the dynamic feedback relationship of complex system by analyzing the integrity and dynamics of system structure. In this paper, the decomposition results of LMDI are taken as the main framework and the factors such as carbon sink [21], carbon emission intensity [22], investment in scientific research [27], and investment in energy-saving technology [28] are also included in the model construction based on the reference of other scholars’ studies on influencing factors of carbon emissions. A system stock flow diagram is constructed to simulate carbon emissions of public buildings, as shown in Figure 2. The carbon emission system of public buildings is divided into four subsystems: economy, society, energy, and environment. The specific indicators of each subsystem are as follows: (1) economic subsystem: GDP, output value of tertiary industry, fiscal revenue, etc.; (2) social subsystem: urban population, public building area, rate of population change, etc.; (3) energy subsystem: energy carbon emission factor, rate of change in energy consumption, total energy consumption, etc.; (4) environmental subsystem: the increase of afforestation area, carbon sink, etc.

There are seven state variables in the model, including total population, urban population, public building area, R&D expenditure, forestry area, energy carbon emission factor, and total energy consumption of public buildings. There are seven rate variables, including total population change, urban population change, area increase, R&D expenditure increase, forestry area increase, carbon emission adjustment coefficient, and energy consumption change. There are 26 auxiliary variables, including urbanization rate, carbon sink, the impact factor of technological progress, the output value of the tertiary industry, the change rate of energy consumption, and the rest are constant.

In this paper, system dynamics VENSIM PLE software is used for simulation calculation. 2019 is taken as the base year, and The period of 2010–2019 is taken as the test year. The parameter variables are determined, and the model is debugged accordingly. The period of 2020–2050 is the simulation prediction period of the system, and the carbon emissions in the next 31 years are predicted. In addition to the initial data sources introduced above, other parameter equations are mainly obtained by the following methods: (1) regression analysis method determines the formulas of GDP, fiscal revenue, tertiary industry output value, etc. and converts them into constant prices in 2010, excluding the impact of inflation; (2) table function method: some variables in the model are not simple linear relationships. Using table functions can accurately describe parameter changes, such as GDP growth rate, R&D expenditure growth rate, forestry area increase, and population change rate; (3) we refer to the relevant literature to determine parameters, such as energy demand elasticity [14, 23], energy structure impact factors [13], and carbon sinks [7, 24]; (4) the influence factors of carbon emission intensity and technological progress were obtained through continuous simulation and testing of the model.

The main parameter equations required to build the model are as follows:where INTEG is an integer function, and 134091 is the initial value.

5.2. Validation of the SD Model

In order to test whether the results of model operation can reflect the law of system development, the model is tested as follows: (1) Stability test: three simulation modes with the step size of one quarter, half a year, and one year were selected in the software to test the stability of the system operation. The test results are shown in Figure 3. The results show that the model is not sensitive to most parameter changes and the model has passed the stability test. (2) Historical test: the total population, GDP, and carbon dioxide emissions of public buildings during 2010–2019 were compared with the actual situation during the same period. The comparison results are shown in Table 3.

From the historical test results, it can be seen that the data error of the total population, GDP, and carbon dioxide emissions of public buildings output from the system operation from 2010 to 2019 are all below 7%. Among them, the average error of the total population and GDP is 1%, and the average error of the carbon dioxide emissions of public buildings and the energy-saving report of Tsinghua University is 2%. This is far less than the 15% error allowed by the system dynamics model, which shows that the model operation results are basically consistent with the actual situation, and the established model can be simulated and predicted.

5.3. Scenario Simulation Setting

The analysis of LMDI factor decomposition results in public buildings shows that the main way to reduce the carbon emission of public buildings is to reduce the carbon emission coefficient by optimizing the energy structure and promoting the development of green technology. At the same time, on the premise of ensuring stable economic development, carbon emissions from public buildings can also be reduced by reducing energy consumption intensity and energy consumption of public buildings and, combined with the SD model operation, it shows that the enhanced forest carbon sink capacity can reduce the carbon emissions of public buildings to determine the control measures. Therefore, this article sets up four scenarios to simulate the peak carbon emissions of public buildings under different scenarios. Among them, economic development is reflected by GDP growth rate, science and technology input ratio and education input ratio reflect green technology development, energy structure adjustment factor, and energy demand elasticity are used to express energy structure adjustment, carbon conversion coefficient, and increase in afforestation area represent forest carbon sink ability. Regarding these 7 variables as control variables, the parameter settings of the control variables of each scheme are shown in Table 4. The specific scenario settings are as follows.

Scenario 1 is the baseline scenario. It is assumed that the model operates according to the current situation and each indicator variable maintains the current development trend to predict the changing trend of carbon emissions from public buildings.

Scenario 2 is an energy-saving scenario, which mainly considers the impact of slowing GDP growth rate and optimization of energy structure on carbon emissions of public buildings. Among them, the GDP growth rate and the optimization of the energy structure are predicted by the “Fourteenth Five-Year Plan”. In the future, Chinese economic operations will be overall stable, and more attention will be paid to the quality of economic growth. The “Plan” points out that it is necessary to accelerate the promotion of green and low-carbon development, develop green buildings, and improve the optimal allocation of energy resources. The specific performance is to adjust the energy structure adjustment factor, energy demand elasticity, and GDP growth rate on the basis of scenario 1.

Scenario 3 is the decarbonization scenario. Based on the existing data in Scenario 2, the carbon emissions of public buildings can be reduced by increasing the proportion of scientific research investment and education investment. The plan refers to the Statistical Bulletin on Science and Technology Input in 2019, which shows that the national science and technology input in 2019 increased by about 13% from the previous year, with both central and local science and technology input maintaining rapid growth. In addition, the national policy of encouraging and supporting science and technology innovation is gradually implemented, and the policy environment is improving.

Scenario 4 is a carbon-negative scenario. On the basis of Scenario 3, it is considered to increase the afforestation area to enhance the carbon sink’s carbon-negative capacity. The plan refers to the “National Master Plan for Major Ecosystem Protection and Restoration Projects (2021–2035).” The policy proposes to increase the total amount of ecological resources on a large scale. It also points out that the phased goals for 2025 and 2035, and by 2035, the forest coverage rate needs to reach 26%. According to the current forest coverage rate and existing area, the annual increase in afforestation area should be 7.33 million hectares. In addition, according to Shi Xiaoliang’s [24] research on forest carbon sink accounting, in addition to the carbon sequestration capacity of tree biomass and understory plants, it is also necessary to consider the amount of carbon sequestration of forest land. Therefore, the carbon conversion factor of this scenario combines the three-carbon conversion factors.

5.4. Simulation Results of Peak Carbon Emission from Public Buildings

The above four scenarios are simulated and compared, and the simulation results are shown in Figure 4 and Table 5.

The analysis shows that the forecast trends of public building carbon emissions in the above four scenarios are basically the same. After a period of rapid growth, the trend gradually slows down to zero growth and then begins to grow negatively. In the baseline scenario, carbon emissions will slowly rise at an average annual growth rate of 2.3% from 2020 to 2041, reaching a peak of 1.242 billion tons in 2041, and then start to grow negatively, and carbon emissions will be 971 million tons by 2050. Under the energy-saving scenario, with the adjustment of the energy structure and the slowdown of GDP growth rate, the carbon peak time is 6 years ahead of schedule, reaching a peak of 959 million tons in 2035, with an average annual growth rate of 1.2%. In the context of decarbonization, accelerating decarbonization of building electrification by increasing scientific research investment can reduce energy carbon emission factors and increase carbon emission reduction efforts. Carbon emissions reached a peak of 874 million tons in 2030, with an average annual growth rate of 0.96%. In the negative carbon scenario, by increasing the afforestation area to increase the carbon sink, the carbon peak time is the same as the decarbonization scenario, with a peak value of 865 million tons.

Through the above analysis, it is found that under the baseline scenario, public buildings will not be able to achieve the goal of peaking carbon in 2030. In the decarbonization scenario and the negative carbon scenario, the carbon emissions of public buildings can reach a peak in 2030, which is 76% lower than the peak carbon emissions in the baseline scenario, which is consistent with the calculation results of He Jincong [25]. Comparing the carbon emission reduction values under different scenarios, it is found that the energy-saving scenario has the greatest carbon emission reduction, followed by the decarbonization scenario, and the negative carbon scenario has the least emission reduction. Compared with the baseline scenario and the negative carbon scenario, the carbon emissions of public buildings are reduced by 805 million tons. Among them, the carbon emission reduction potential generated by the optimization and adjustment of the energy structure is 436 million tons, and the emission reduction contribution rate is 54%. The increase in investment in scientific research and education has a carbon emission reduction potential of 355 million tons, and the emission reduction contribution rate is 44%. The carbon emission reduction potential generated by the increase in afforestation area is 14 million tons, and the emission reduction contribution rate is 2%.

6. Conclusions

This paper takes the carbon emissions of public buildings as the research object and uses the LMDI decomposition results as the support to establish a dynamic model of the carbon emissions system of public buildings in China. Through a comparative analysis of four different scenarios, the peak carbon emission and carbon emission reduction potential of public buildings under the action of changes in different policy indicators are investigated. Research indicates the following:(1)Under the current policy unchanged, with the development of the tertiary industry and urbanization, the carbon emissions of public buildings have increased year by year and reached a peak of 1.242 billion tons in 2041, with an average annual growth rate of 2.3%, and the 2030 carbon peak goal cannot be achieved.(2)Through the operation of the SD model, it can be seen that technological progress requires sufficient knowledge accumulation and time delay to improve energy utilization and reduce energy carbon emission factors. Therefore, it is necessary to actively promote scientific research and innovation related to carbon emission reduction and promote the continuous progress of low-carbon technology.(3)Comparing the four scenarios of the carbon emission system of public buildings, it can be seen that only adjusting the energy structure according to the national plan, or adjusting the investment in scientific research and education according to the policy, or the carbon sink project cannot achieve the goal of peaking the carbon emission of public buildings in 2030. Therefore, it is necessary to adjust these variables as a whole in order to achieve the carbon emission reduction target of public buildings.(4)For the main body of public buildings, the largest contribution to carbon emission reduction is the adjustment of the energy structure, accounting for 54%, followed by the investment in scientific research and education, accounting for 44%. Therefore, in the optimization of the energy structure, on the one hand, it is necessary to steadily reduce the consumption of fossil energy and develop clean energy. On the other hand, it is necessary to vigorously promote green public buildings and build a low-carbon ecological city. In terms of investment in scientific research and education, it is necessary to establish a stable growth mechanism for scientific research investment and strengthen the construction of talent research and development teams.

Data Availability

The data used to support the findings of this study is available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest regarding the publication of this paper.

Acknowledgments

The financial support from the Social Science Planning Fund of Liaoning Province (No. L20BGL029) and Humanities and Social Sciences General Program of Liaoning Provincial Department of Education (No. LJKR0147) is gratefully acknowledged.

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