Stress of urban energy consumption on air environment

Abstract

With rapid urbanization and heavy industrialization as well as the rapid increase of cars in China, the effect of energy consumption on urban air environment is increasingly becoming serious, and has become a hot topic for both scholars and decision-makers. This paper explores the effect mechanism and regulation of urban energy consumption on the air environment, and summarizes the framework of the stress effect relationship and the evolutionary process. In accordance with the effect relationship of the internal factors between the two, analytic approaches studying the stress effect of urban energy consumption on air environment are proposed, including the analysis of air environment effects caused by urban energy consumption structure change, and the analysis of air environment effects caused by urban energy economic efficiency change, as well as a decomposition analysis of air pollutant emission caused by urban energy consumption. Applying the above-mentioned approaches into a case study on Beijing City, this paper analyzes the effect relationship among urban energy consumption structure improvement, energy economic efficiency increase and air quality change since the period when Beijing City officially proposed to bid for the 2008 Olympic Games in 1998. In addition, it further analyzes the effect and contribution of urban industrial activity level, industrial economic structure, industrial energy intensity, and industrial energy structure as well as emission coefficients on the change in industrial SO₂ emission, which can provide valuable information to the government for making comprehensive environmental policies, with the use of the logarithmic mean Divisia index (LMDI) method. It is shown that under the precondition that the industrial economy maintain a continuous and rapid increase, improvements in energy intensity and a decline in emission coefficients are the main means for reducing Beijing’s industrial SO₂ emissions.

Keywords

urban energy consumption / air environment / stress effect / decomposition analysis

Introduction

Objectively speaking, there is an extremely complex stress effect relationship between air environment and urban energy consumption, and it has become a hot topic for both scholars and government decision-makers. Interaction between the two is mainly manifested as follows: On one hand, as a result of highly concentrated urban energy consumption in the regional space (Omer, 2008), the air environment becomes increasingly imbalanced and air pollution follows; on the other hand, with the continuously reinforced awareness and efforts towards environmental protection, it is inevitable that urban air environmental protection will restrict energy consumption.

In fact, studies on the relationship between urban energy consumption and air environment include studies on the impact of energy consumption on different types and utilization patterns of air environment as well as the limit imposed by air environmental protection on the structure and total amount of energy consumption. Since the 1970s, scholars, e.g. Bose (1996), Bose and Anandalingam (1996), Dincer (1999), Chan and Yao (2008), have carried out a series of studies air environmental impact from changes in urban energy consumption structure or sectoral energy use. A host of approaches have been brought forward in these studies, like mathematical planning and thermodynamic analysis, cluster analysis, life cycle evaluation and decomposition analysis (Maréchal and Kalitventzeff, 1997; Ang, 2004; Zhang et al., 2004; Kato and Widiyanto, 2005). However, the internal relationship and general rule between urban energy consumption and air quality change need more adequate explanation; the interactive mechanism between the two need deeper analysis and a responding mechanism needs to be established to study the stress effect under the same framework (Ang, 2007). In conclusion, on the basis of existing achievements, further studies on the relationship between the air environment and urban energy consumption stress is indispensable.

With rapid urbanization and heavy-industrialization, along with a rapid advance in the vehicle boom in China, urban energy consumption is dramatically on the rise. What is worse, coal accounts for 70% of the energy consumption in China, further exacerbating air conditions (Chai et al., 2006; Yu, 2006). A series of measures have been taken to alleviate urban air pollution in recent years, but the situation has not fundamentally changed (Yi et al., 2007), and has become more complicated in some regions. Incidents like photochemical pollution and small-sized particle contamination have been constantly reported in some metropolises. That is to say, the relationship between energy consumption and environmental pollution must be systematically studied, and the mechanism of influence of energy use mode on pollutant emission should be analyzed so that a universal rule can be summarized to explain the process of urban energy consumption and air environmental change, laying a scientific foundation for a solution to urban environmental contamination caused by energy use.

Stress mechanism of urban energy consumption on air environment

Process of urban energy consumption

Based on various consumption styles, urban energy consumption is mainly divided into five sectors: industry activities, household life, business activities, transportation and others. For industry activities, transformation in the mode of production results in a change in energy factor consumption. For household life, business activities and transportation, energy use is associated with commodity consumption. Collectively, energy factor consumption and energy commodity consumption contribute to changes in urban energy consumption activities, and these determine energy-related air pollutant emissions and affect air quality. In the meantime, urban air environmental protection requires that a limit be imposed to restrict energy consumption in all kinds of activities, as shown in Fig. 1.

Concept framework of the stress effect

With changes in urban energy consumption activities, various forms of impact become imposed on the air environment. In order to make an accurate statement on the whole process, characteristics of urban energy consumption must be outlined to set up an indicative system for reflecting internal characteristics and the development situation. The characteristics of urban energy consumption can be represented by scale, intensity, structure, application of clean technology and spatial distribution. Here, scale means total amount of urban energy consumption, and intensity is a reflection of energy use efficiency and service level that can be represented by energy economic efficiency to indicate an economy’s dependence on energy. Structure refers to grade, quality and diversity of energy, a key factor influencing urban air conditions. Structure and intensity are important indicators when judging if urban energy consumption is sustainable. Clean technology reflects an energy consumption’s environmental friendly level and directly determines emissions of contaminated gas when structure and intensity are fixed. Taking clean coal technology as an instance, it includes techniques applied in the whole course from coal processing, conversion and combustion to pollution control. Spatial distribution of energy consumption is closely associated with division of urban environmental functions and reasonable utilization of environmental carrying capacity.

Compared with the factors mentioned above to express urban energy consumption characteristics, urban air environmental characteristics can be described by three key indices which include the totality factor, quality factor and function factor, representing total air pollutant emission, air quality and division of environmental functions, respectively. A concept framework can be built to feature urban energy consumption’s stress effect on the air environment, as shown in Fig. 2.

Evolution of the stress effect

In the course of urban development, remarkable transformation and progress have constantly been made in urban energy consumption structures, energy economic efficiency and clean energy technology as a result of upgraded social and economic structures, increasing residential consumption and a growing urban population. As urbanization deepens, changes in urban energy consumption inevitably result in a transformation of the air environment in terms of interaction processes, intensity and level. The interaction between urban energy consumption and the air environment is represented in various forms at different stages. In the long run, as urban energy consumption structure evolves and more and more clean energy technologies are developed and applied, an overturned “U” model can be used to describe the relationship between the air pollution index and gross energy consumption or per capita energy consumption, as shown in Fig. 3. According to changes observed in the characteristics of urban energy consumption and its effect on the air environment, the process of the effect can be divided into five stages.

1) Stage of rudimentary coordination. This stage generally takes place at the early phase of urban development when energy resources are moderately consumed with slow economic expansion. As a result, air is not heavily polluted and urban energy consumption has little impact on the air environment; air contamination caused by energy use can be basically cleared as nature is capable of self-cleaning.

2) Stage of antagonistic effect. This stage is followed by signs of increasingly growing urban energy consumption and significantly rising pressure on air pollution control. As urban energy consumption increases rapidly, its impact on the air environment experiences dramatic changes, and the antagonistic effect becomes complicated between the two and accumulative environmental response intensifies rapidly.

3) Stage of breaking-in. This stage appears as rapid growth in urban energy consumption and is expected to come to an end when deteriorating air pollution moderates as a result of extensive application of clean energy technology. Following a sharp increase in urban energy consumption, rising environmental pressure comes close to or even surpasses nature’s self-cleaning capability. This stage is marked by an alternation of conflict between urban energy consumption and the air environment, which softens and re-intensifies.

4) Stage of amelioration. This stage comes when growth in urban energy consumption slows down and pressure on air conditions is continuously relieved as clean energy technologies are put into practice. Advanced energy technologies are extensively applied and the energy consumption structure is adjusted. As a result, the air’s condition improves and pressure on environmental protection relaxes, and conflict between urban energy consumption and the air environment becomes less and less intense and accumulative environmental response shows a sign of steady fall.

5) Stage of high-grade coordination. In general, this stage emerges when urban energy consumption has stabilized. Destruction by urban energy consumption of the air environment has been recovered and pressure on environmental protection decreased significantly. The conflict between urban energy consumption and the air environment is basically eliminated, and a harmonious symbiosis exists. Accumulative environmental response shows a sign of delaying steady trend.

Analysis approach of the stress effect

Analysis approach of air environment effect caused by change of urban energy consumption structure

Due to the existence of alternative energy, a variety of factors will collectively contribute to changes in energy consumption structure. As time goes by, the structure constantly varies under the influence of external disruption and internal fluctuation. In terms of space dimension, the structure diversifies in different regions, various urban functions and distinct economic levels. The theory also holds water in perspective of time dimension, as the economic structure is upgraded, individual consumption level lifts up, and urban energy consumption structure is transformed, leading to various impacts on the urban air environment in the form of a chain reaction.

For the purpose of quantitatively describing the relationship between changes in urban energy consumption structure and the air environment, the concept of Energy Consumption Structure Clean Diversification (ECSCD) is put forward. The clean diversification coefficient is benchmarked with coal consumption, which is known as the dominant mineral resource in the early stage of industrialization. Having the greatest impact on the environment, diversification is made on that basis for clean alternative energy resources like petroleum, natural gas, hydropower and nuclear energy. Adding up the diversification results and the coefficient the ECSD can be figured out according to the formula listed below:

(1)ECSCD={\displaystyle \sum (C/C,\alpha O/C,\beta G/C,\gamma E/C)},

where ECSCD stands for energy consumption structure clean diversification; C represents the coal consumption amount (tce); α is the average difference coefficient of air pollutant emissions from consumption of coal and petroleum with the same thermal value; O represents the petroleum consumption amount (tce); β is the average difference coefficient of air pollutant emissions from consumption of coal and natural gas with the same thermal value; G represents the natural gas consumption amount (tce); γ is the average difference coefficient of air pollutant emissions from consumption of coal and hydropower or nuclear energy with the same thermal value and E represents the consumption amount of hydropower, nuclear energy and other renewable energy resources (tce).

To reflect and evaluate a city’s air integrated pollution level, the concept of Air Integrated Pollution Index (AIPI) (Ministry of Environmental Protection of the People’s Public of China, 2007) is introduced here to normalize related pollutants concentration respectively in accordance with environmental quality standards, hence we get the simple nondimensional index by adding them up, which reflects the air pollution situation of a region comprehensively, and can be used to compare the relative extent of air integrated pollution among different cities. The formulas are:

(2)P={\displaystyle \sum _{i=1}^n{P}_i},

(3)P_i={\displaystyle \frac{C_i}{S_i}},

where P stands for AIPI; P_i represents the fractional index of the ith air pollutant; n is the pollutant number; C_i is the annual average concentration value of the ith air pollutant (mg/m³) and S_i is the environmental quality standard limit of the ith air pollutant (mg/m³).

Introducing the concepts of ECSCD and AIPI, the relationship between changes in urban energy consumption structure and air environmental quality can be established efficiently, hence the prediction function can be realized. We can predict the impact of future urban energy consumption structure changes on air environmental quality; or vice versa, from the prospect of environmental protection, predict the requirements of urban air quality improvement on the adjustment of energy consumption structure.

Analysis approach of air environment effect caused by change of urban energy economic efficiency

Energy economic efficiency is another very important index for describing the characteristic of urban energy consumption. In general, energy economic efficiency is used as a primary factor to measure or evaluate energy efficiency in a city. The so-called economic efficiency refers to energy consumption per unit economic output, which is used to indicate the link between total energy consumption and economic growth. Energy consumption is integrated on the basis of a variety of factors like the level of urban development, economic structure, energy structure, equipment, technology and management level. Generally, energy economic efficiency is described as energy consumption per unit GDP, a term appearing in macro economics.

Since energy economic efficiency directly influences total urban energy consumption and reflects the economic structure, technology and comprehensive management level in a city, its impact on air environment quality is great (Rosen and Dincer, 2001). More and more attention has been paid to researches on energy economic efficiency all around the world. In order to better track any quantitative link between energy economic efficiency and urban air environment, environmental pollution factors closely associated with urban energy consumption such as SO₂, NO₂, PM₁₀, etc. can be chosen to establish a direct link between the two by means of an integrated approach like mathematical statistics to achieve the goal of reflecting economic efficiency’s impact on the air environment.

Decomposition analysis method of air pollutant emission caused by urban energy consumption

To understand the impact mechanism of energy consumption on air pollutant emission, more and more scholars have focused on the application of the decomposition method (Ang, 1994; Ang and Choi, 1997; Ang and Liu, 2001; Ang, 2004; Ediger and Huvaz, 2006). On one hand, they can find the main factors impacting air pollutant emission through decomposition analysis; on the other hand, they also hope to provide more scientific evidences for the improvement of urban air quality and reduction of pollutant emission. With the increasing maturity of decomposition analysis theory, hosts of influencing factors have been added, such as sector energy consumption structure, sector energy intensity as well as various energy emission coefficients. In the development process of the decomposition approach, the logarithmic mean Divisia index (LMDI) has received more attention due to its characteristics, which include no residuals, comparatively complete theoretical foundation, strong suitability and easy to apply. In terms of application scope, LMDI can be used to analyze the data of a time series, and can also be used to calculate non-positive values, therefore it has already become one of the commonly used decomposition approaches (Ang, 2004).

Based on LMDI, the air pollutant emission of urban industrial energy consumption can be decomposed into industrial activity level, industrial economic structure, sector energy intensity, sector energy structure as well as various energy emission coefficients of different sectors. The formula is as follows:

(4)Q={\displaystyle \sum _{ij}{Q}_{ij}}={\displaystyle \sum _{ij}Y{\displaystyle \frac{Y_i}{Y}}}{\displaystyle \frac{E_i}{Y_i}}{\displaystyle \frac{E_{ij}}{E_i}}{\displaystyle \frac{Q_{ij}}{E_{ij}}}={\displaystyle \sum _{ij}Y{S}_i}{I}_i{M}_{ij}{U}_{ij},

where Q is the total amount of pollutant emission (t); i stands for industrial sector; j stands for energy category; Q_ij represents the pollutant emission amount of energy type j of sector i (t); Y is the gross industrial output (yuan); Y_i is the output of the ith sector (yuan); E_i is the total energy consumption amount of the ith sector (tce); E_ij represents the consumption amount of energy type j of sector i (tce); S_i is the proportion of the ith sector’s output on gross industrial output; I_i is the energy intensity of the ith sector (tce/yuan); M_ij, is the proportion of energy type j consumption amount on the gross energy consumption amount of sector i and U_ij is the pollutant emission coefficient of energy type j of sector i (t/tce).

The contributions of each influencing factor on changes in pollutant emission amount can be gained through additive decomposition, listed as Eqs. (5-10).

(5)\mathrm{\Delta }{Q}_{tot}=Q^T-Q^0=\mathrm{\Delta }{Q}_{act}+\mathrm{\Delta }{Q}_{str}+\mathrm{\Delta }{Q}_{\mathrm{int}}+\mathrm{\Delta }{Q}_{mix}+\mathrm{\Delta }{Q}_{emf},

(6)\mathrm{\Delta }{Q}_{act}={\displaystyle \sum _{ij}{\displaystyle \frac{Q_{ij}^T-Q_{ij}^0}{\ln Q_{ij}^T-\ln Q_{ij}^0}}}\ln \left({\displaystyle \frac{Y^T}{Y^0}}\right),

(7)\mathrm{\Delta }{Q}_{str}={\displaystyle \sum _{ij}{\displaystyle \frac{Q_{ij}^T-Q_{ij}^0}{\ln Q_{ij}^T-\ln Q_{ij}^0}}}\ln \left({\displaystyle \frac{S_i^T}{S_i^0}}\right),

(8)\mathrm{\Delta }{Q}_{\mathrm{int}}={\displaystyle \sum _{ij}{\displaystyle \frac{Q_{ij}^T-Q_{ij}^0}{\ln Q_{ij}^T-\ln Q_{ij}^0}}}\ln \left({\displaystyle \frac{I_i^T}{I_i^0}}\right),

(9)\mathrm{\Delta }{Q}_{mix}={\displaystyle \sum _{ij}{\displaystyle \frac{Q_{ij}^T-Q_{ij}^0}{\ln Q_{ij}^T-\ln Q_{ij}^0}}}\ln \left({\displaystyle \frac{M_{ij}^T}{M_{ij}^0}}\right),

(10)\mathrm{\Delta }{Q}_{emf}={\displaystyle \sum _{ij}{\displaystyle \frac{Q_{ij}^T-Q_{ij}^0}{\ln Q_{ij}^T-\ln Q_{ij}^0}}}\ln \left({\displaystyle \frac{U_{ij}^T}{U_{ij}^0}}\right),

where ΔQ_tot stands for the change in pollutant emission amount, by comparing year T with the first year (t); Q_T represents the pollutant emission amount for year T (t); Q₀ represents the pollutant emission amount for the first year; ΔQ_act is the pollutant emission amount change caused by gross output change (t); ΔQ_str is the pollutant emission amount change caused by industrial economic structure change (t); ΔQ_int is the pollutant emission amount change caused by energy intensity change (t); ΔQ_mix is the pollutant emission amount change caused by energy structure change (t) and ΔQ_emf is the pollutant emission amount change caused by emission coefficient change (t).

Case study

Outline of energy consumption and air quality of Beijing City

Beijing City, Capital of China and the host city of the 29th Olympic Games, made a solemn promise of a “green Olympics” to the whole world in 2001. To achieve the target, a large amount of positive and efficient work in the aspect of urban environmental protection has been done since Beijing officially proposed to bid for the Olympic Games in 1998. Through nearly 10 years’ continuous effort, environmental quality in Beijing has improved a lot. The total days reaching a Grade II standard of urban air quality per year increased to 246 in 2007 from 100 in 1998, increasing by 1.46 times.

To achieve the air environmental quality target for the Olympic Games, the following four measures were taken. First, insisting on an intensive development road and fully improved energy use efficiency. In 2007, the unit GDP energy consumption ratio of Beijing decreased by more than one time, as shown in Fig. 4. Second, a further adjustment in the energy consumption structure was observed, with greatly increased natural gas and petroleum proportions in the primary energy consumption. Till 2007, the ratio of coal accounting for primary energy consumption was already less than 50%. Third, increases in environmental protection investment for pollution control. Since 1998, hundreds of billions of RMB has been invested into air pollution control. Fourth, enhanced cooperation with the surrounding provinces and cities for air pollution control, and the Olympic air environmental quality guarantee joint control system was established with Tianjin City, Hebei province, Shanxi province, Inner Mongolia and Shandong province. Through the above efforts, Beijing’s air environmental quality was improved greatly. Compared with 1998, the annual average concentration of SO₂ decreased by 60.5%, that of NO₂ decreased by 45.4%, and that of PM₁₀ decreased by nearly 20%, and the AIPI decreased by 32.5% in 2007, as shown in Fig. 5.

Stress effect of Beijing City’s energy consumption on air quality

It can be seen that a highly linear relation exists between the improvement of energy economic efficiency and air quality by analyzing the relationship of the changes in Beijing’s energy economic efficiency and air quality since 1998, as shown in Fig. 6. This indicates that the present energy economic efficiency factor plays a key role in Beijing’s air quality improvement because energy economic efficiency is a comprehensive index, which not only relates to a region’s industrial and energy consumption structure but also reflects the changes in the region’s industrial technological level, including end pollution control technological levels. In conclusion, improving energy economic efficiency is the future first choice for Beijing to improve its air quality.

Here, ECSCD is used to indicate energy structure change and AIPI is used to indicate the integrated improvement of air quality in order to reflect the stress effect of energy consumption structure on air quality. Through simulating the relationship between the two, it can be seen that the energy consumption structure improvement is also related to Beijing’s air quality improvement in the recent 10 years. The AIPI shows a steady declining trend with improvements in energy consumption structure. According to the change scale, the impact of energy consumption structure optimization on air quality improvement was reduced with the gradual improvement of air quality, as shown in Fig. 7.

Based on the above analysis, a conclusion can be made through investigating the course of evolution that the stress effect of Beijing’s energy consumption on air quality is in “the stage of amelioration”, which indicates the continuing improvement of clean energy usage levels and the lasting reduction of air pollution pressure according to the evolutionary process.

Industrial SO₂ emission decomposition analysis in Beijing

Data sources

This study carried out a SO₂ emission factor decomposition analysis on Beijing’s 40 industrial sectors during the period of 2003-2007. The data used included the amount of SO₂ emission, industrial added value, various energy consumption amounts, and SO₂ emission coefficients of different energy consumption of various industrial sectors from 2003 to 2007. The energy type included four main energy resources, namely coal (raw coal, cleaned coal, other washed coal), coke, petroleum products (crude oil, gasoline, kerosene, diesel oil, fuel oil, LPG) and natural gas.

The data for various industrial added value and energy consumption amounts were obtained from the Beijing Statistics Yearbook (2003-2007); SO₂ emission data from the Beijing pollution source statistics table (2003-2007); SO₂ emission coefficients of different levels of energy consumption from 2003 to 2007 were obtained by calculating from the pollution source statistics base table.

Results and discussions

Basing on the LMDI method, we analyzed the effect and contribution of various factors on Beijing’s industrial SO₂ emission change since 2003.

1) Integrated decomposition results

The integrated effects caused by five major factors on the change in Beijing’s industrial SO₂ emissions are shown in Table 1. According to the decomposition results, the effects of different factors on SO₂ emission change are much more different. Since the industrial activity level is enhanced and the industrial structure has been transferring to heavy-industrialization, industrial activity and economic structure have generated positive effects on SO₂ emission increase in Beijing, with annual average increases of 20.40 thousand tons and 4.84 thousand tons, respectively. Because energy intensity and the emission coefficient generated negative effects on SO₂ emission increase, the two factors are thought to be the main driving forces guaranteeing the decline in industrial SO₂ emission. The energy structure factor has a less important effect on industrial SO₂ emission change, which reflects a steady industrial energy structure situation.

By carrying out further analysis on the above results, it is agreed that decreasing the emission coefficient and energy intensity have been the most efficient means to achieve greater reduction in SO₂ emission amounts in recent times, under the precondition of maintaining a continuous and rapid industrial economic rise. At the same time, the effect of energy structure adjustments on reducing SO₂ emission should be further explored.

2) Effect of industrial activity on industrial SO₂ emission change

The decomposition results on Beijing’s industrial SO₂ emission changes from 2003 to 2007 indicate that the rapid increase of the industrial economy contributed a lot to the increase in SO₂ emissions. Fig. 8 lists the annual average effects of the changes in industrial activity level on industrial SO₂ emission change from 2003 to 2007. Among the 40 industrial sectors, the economic increase in four industrial sectors including electricity and hot power production and supply, smelting and processing of ferrous metals, nonmetal mineral products, raw chemical materials and chemical products have the largest effects on industrial SO₂ emission change, accounting for 90.9% of the total industrial activity effects. Hence, a suitable limit on the speed of economic increase of the four industrial sectors will have a significant effect on decreasing Beijing’s SO₂ emissions.

3) Effect of industrial economic structure on industrial SO₂ emission change

The effect of industrial economic structure on industrial SO₂ emission change also mainly concentrates on the four sectors including electricity and hot power production and supply, smelting and processing of ferrous metals, nonmetal mineral products, raw chemical materials and chemical products. However, the difference is that due to the decreased share proportion of nonmetal mineral products, raw chemical materials and chemical products in the industrial economy, their effects on industrial SO₂ emission change are negative. On the contrary, due to the increased proportion of the former two sectors in the industrial economy, their effects on industrial SO₂ emission increase are positive. Since the effect of electricity and hot power production and supply on industrial SO₂ emission change is huge, this results in a change in industrial economic structure; hence it still generates a positive, increasing effect on industrial SO₂ emissions.

4) Effect of energy intensity on industrial SO₂ emission change

Similar to other cities in China, improvement in energy intensity is one of the most important factors driving the decline in industrial SO₂ emission in Beijing. From 2003 to 2007, the energy intensity changes in Beijing’s electricity and hot power production and supply, and smelting and processing of ferrous metals have resulted in an annual average decrease of 17.15 thousand tons, that is about 20% of Beijing’s gross industrial SO₂ emission amount in 2007. However, the other dozen sectors, including raw chemical materials and chemical products, have reverse results, having a certain negative effect on reducing industrial SO₂ emission. However, the effect is limited, as shown in Fig. 9.

5) Effect of energy structure on industrial SO₂ emission change

Industrial energy structure has the least effect on Beijing’s industrial SO₂ emission change compared with the other factors, which is due to Beijing’s industrial energy structure remaining steady in recent years and its characteristic relying on coal as its main energy source for a long time. However, the situation for household energy is quite different. Household SO₂ emission has declined greatly through adjusting household energy structures in recent years. Among the 40 industrial sectors, electricity and hot power production and supply have shown some improvement in terms of energy structure, as shown in Fig. 9, which is because in the recent years Beijing transformed its heating system into central gas heating on a large scale.

6) Effect of emission coefficient on industrial SO₂ emission change

Emission coefficient change is another important factor favorable to the decline of industrial SO₂ emission other than energy intensity. Fig. 10 indicates that SO₂ emissions of almost all sectors have been declining from 2003 to 2007, and the reason is that strict environmental protection requirements for industrial enterprises were implemented to achieve the “green Olympics” target of Beijing. According to the target, all major industrial enterprises with SO₂ emission were required to build desulphurizing facilities, and some other enterprises unable to build these facilities also actively adopted measures reducing sulfur shares in their fuels. These measures efficiently guaranteed the large-scale decline of Beijing’s industrial SO₂ emissions.

Conclusions

Through this study, we know that there is an extremely complex stress relationship between air environment and urban energy consumption. Different energy consumption scales, energy structures, energy intensities and pollutant control technology levels will all have different effects on urban air environmental quality. With the development of the social economy, the states of these characteristic factors will keep changing and drive the effect of transformation of urban energy consumption on the air environment in terms of ways of interaction and intensity. In the long run, the evolutionary process of urban energy consumption’s effect on the air environment can be divided into five stages, namely, rudimentary coordination, antagonistic effect, breaking-in, amelioration and high-grade coordination.

In order to quantitatively describe the air environment effect caused by change in the urban energy consumption structure, the concepts of ECSCD and AIPI were introduced to establish the approach analyzing the relationship between energy consumption structure change and urban air quality. By applying the approach in Beijing, it is clear that in the recent 10 years there has been an obvious relationship between the optimization of Beijing’s energy consumption structure and air quality improvement. With the improvement of the energy consumption structure, AIPI has been declining steadily in Beijing. Additionally, we simulated the change relation between Beijing’s energy economic efficiency and air quality since 1998, which showed a highly linear relation between improvement in energy economic efficiency and air quality. It was revealed that improving energy use efficiency plays a key role in improving urban air quality in Beijing City.

The LMDI method was adopted to make a decomposition analysis on Beijing’s industrial SO₂ emission in order to further analyze the effect and contribution of industrial activity level, economic structure, energy intensity, energy structure and emission coefficient on industrial SO₂ emission change. The results showed that under the precondition that the industrial economy maintain a continuous and rapid increase, improvement in energy intensity and a decline in emission coefficients are the main means to reduce Beijing’s industrial SO₂ emission. In addition, the case study also proves that the LMDI decomposition method is a good one for analyzing the stress effect of urban energy consumption on the air environment. By adopting it, more useful information can be decomposed at the industrial sector level, which is favorable for government decision-makers to formulate well-coordinated energy environmental policies; hence a comprehensive management and coordinated development of urban energy and the environment can be achieved.

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