Global warming negatively affects the sustainable development of both ecosystems and the global environment. As a result, conserving energy and reducing carbon emissions have become common global goals to combat climate change. The Chinese government has committed to reducing carbon emissions per unit gross domestic product (GDP) by 40%–45% by 2020 compared to the level in 2005. In the 21st Conference of Parties to the United Nations Framework Convention on Climate Change, the Chinese government further committed to reduce carbon dioxide emissions per unit GDP by 60%–65% compared to the 2005 level, with the peak carbon dioxide emission being reached by 2030. China distributed the emission reductions specifically to local governments during the 12th Five-Year Plans and will continue this policy during the 13th Five-Year Plans [1
]. In this context, an in-depth study of the trends in urban energy consumption and influencing factors will help achieve the overall goals of saving energy and reducing emissions.
The city of Tangshan, locating in Hebei Province, is a resource-based city that is rich in coal mines and has a prosperous iron-steel industry. Because of the prevalence of heavy industry, Tangshan has a strong and invariant demand for energy, and carbon emissions are a prominent problem [3
]. In this context, an in-depth study of trends in urban energy consumption and the factors that influence them contribute toward the overall goals of saving energy and reducing emissions. The main purpose of this paper is to identify the main factors contributing to the change in energy consumption in Tangshan. To realize this goal, a decomposition technique is employed to analyze the influential factors. Currently, structural decomposition analysis (SDA) and index decomposition analysis (IDA) are the most-used decomposition techniques for energy consumption and carbon emissions [6
Of these techniques, the first (SDA) breaks down already decomposed variants on the basis of known economic relationships and mathematical rules, which broadens functionality and makes for a more thorough, convenient, and systematic analysis [7
]. SDA is thus usually based on input–output tables and has been widely applied in studies that deal with energy and environmental issues [8
]. In addition, Su and Ang [9
] developed the multiplicative SDA method, proposing the further use of attribution analysis via the generalized Fisher index in the context of structural decomposition analysis, while in order to evaluate performance indicators for multi-regional comparisons, these workers also put forward a spatial-SDA framework for analysis [10
Wachsmann, Weber, Kim, and Cellura and co-workers [11
] used SDA to analyze the changes in energy consumption in Brazil, America, Korea, and Italy, respectively. They found that structural changes contribute the most to reductions in energy consumption. Since the reform and opening up in China, energy consumption has increased quickly and continues to rise; for example, energy consumption increased from 1549 Mtce in 2001 to 4260 Mtce in 2014 [15
]. Scholars have paid much attention to the reasons for the growth in energy consumption in China. Xia, Li, Zhang, Zheng, and Zhao and co-workers [16
] all applied SDA to analyze the main factors that have contributed to changes in Chinese energy consumption in different stages and showed that end-use increases have tended to cause this growth. In addition, other researchers have considered changes in energy consumption at the provincial level [22
The alternative approach, IDA, mainly consists of Laspeyres index decomposition (LID) and Divisia index decomposition (DID). Compared with DID, the multiplier decomposition relation is difficult to separate in LID. Thus, DID is widely used in decomposition analysis. With the continuous development of DID, logarithmic mean Divisia index (LMDI) becomes more complete. In the calculation of decomposition, LMDI can completely decompose the remainder with non-explainable remainders. LMDI can be divided into LMDI-I [23
] and LMDI-II [24
]. LMDI-I and LMDI-II will not produce an explainable remainder. However, the estimates of the effects of energy consumption change given by the two methods tend to differ slightly. Earlier studies that compared the two methods from the viewpoint of index numbers found that both have their strengths and weaknesses. Both methods satisfy most of the tests of index numbers, which are considered to be relevant to IDA; while both satisfy most index number tests considered relevant to IDA, additive LMDI-I fails the proportionality test, while additive LMDI-II fails the aggregation test [25
LMDI is widely used for the quantitative study of factors that contribute to changes in energy consumption and carbon emissions because its data requirements are not high, and there is no unexplained residual. From the viewpoint of spatial scale, LMDI can be used at the country level, for example, to study energy consumption and carbon emission in China, the European Union, and other areas [26
]. Many studies have also been carried out on energy consumption and carbon emissions at the provincial level [32
], while such studies are relatively rare at the city level [38
]. Research has also been conducted at the sector level; for example, Choi and Oh studied the energy consumption and carbon emissions associated with the Korean manufacturing industry [39
], and Zhang and co-workers investigated the energy consumption of transportation services in China [40
]. In addition to the SDA and the LMDI, the DEA method is sometimes also applied to decompose factors associated with changes in energy efficiency [5
As described above, LMDI has many advantages. However, LMDI does not take intermediate input into consideration and thus ignores the changes in energy consumption attributed to energy consumption structure between sectors [42
]. As a result, an increasing number of studies have used the SDA-LMDI model to analyze the forces driving changes in the economic system [43
], energy consumption and energy intensity [11
], and CO2
]. Building on these earlier studies and considering energy consumption as a variable that can be decomposed, this paper applies a new method that incorporates the advantages of both the LMDI and input–output approaches, enabling complete decomposition and explaining how energy consumption structure affects changes in energy consumption. Further, because studies that deal with energy consumption changes at the level of cities are relatively rare, we chose the city of Tangshan as a case study in order to develop a better understanding of environmental problems in the Beijing-Tianjin-Hebei region as well as the characteristics of energy consumption in China. The remainder of this article is organized as follows. Section 2
describes the LMDI method and the data used. Section 3
presents the trends in industrial structure and energy consumption in Tangshan city. Section 4
presents the effects of different factors on the changes in energy consumption in Tangshan city based on LMDI. Section 5
presents the conclusions and offers some policy implications.
Decomposition results for energy consumption in the city of Tangshan between 2007 and 2012 are listed in Table 2
and Table 3
Total energy consumption increased from 2007 to 2012 in most sectors, except for water production and supply (S24), coal mining and allied production (S2), food and tobacco (S6), oil and gas products (S3), paper printing and stationery (S10), and electricity and heat production and supply (S22). Among the sectors, electricity and heat production and supply (S22) had the largest reduction in energy consumption (237,211 tce), followed by paper printing and stationery (S10), oil and gas mining (S3), and tobacco, food, and beverage industries (S6), as well as the coal mining and allied products industry (S2). The energy consumption in all other sectors increased from 2007 to 2012. The metal productions fabrication industry (S15) had the largest increase (3,087,521 tce), followed by construction (S25), metal smelting and rolling processing (S14), chemicals (S12), and special equipment manufacturing.
In terms of reducing energy consumption, technical changes proved to be the most important factor. The three sectors with the largest reduction in energy consumption attributable to technical change were metal smelting (1,529,898 tce), non-metallic minerals (343,478 tce), and construction (322,179 tce). Input structural effects led to increases in energy consumption in most sectors. Metal smelting and rolling processing (S14), construction (S25), and chemicals (S12) were the three sectors with the largest increase in energy consumption caused by structural change, mainly because of increased production in these industries. The demand for intermediate inputs also increased, resulting in increased demand for intermediate energy consumption. Finally, final use scale effects also increased energy consumption in all sectors; the largest such increases were seen in the metal smelting and construction industries (S14) as this sector experienced the greatest production growth.
Data show that in terms of sectors that experienced an increase in energy consumption between 2007 and 2012, transport equipment (S17) exhibited the largest increase, by more than 200%, compared to other industrial activities (S21), manufacture of fabricated metal products (S15), electrical machinery and apparatus (S18), and the mining of nonmetal (S5). In terms of how each factor affected changes in energy consumption relative to 2007, data also show that 24 of the 28 experienced negative technical changes, which means that the majority made technical improvements to reduce energy consumption. Other industrial activities (S21), manufacture of fabricated metal products (S15), paper and products for culture, education and sports (S10) had the greatest technical effect. As for input structural effect, 24 of 28 sectors had positive effect in increasing energy consumption, manufacture of fabricated metal products (S15), construction (S25), steam supply (S23), electrical machinery and apparatus (S18), and textile (S7), which all had an over 50% energy consumption increase. In term of final use structural effect, about half of the sectors had a positive effect in increasing energy consumption. The other industrial activities (S21), manufacture of fabricated metal products (S15), electrical machinery and apparatus (S18), mining of nonmetal (S5), and mining of metal (S4) had the most obvious final use structural effect in increasing energy consumption, while mining of oil and gas (S3), mining of coal (S2), paper and products for culture, education, and sports (S10), and other service activities (S28) had the most significant final use scale effect in decreasing energy consumption. All sectors had a positive final use scale effect in increasing energy consumption. Sectors like other industrial activities (S21), manufacture of fabricated metal products (S15), and electrical machinery and apparatus (S18) had an 100% increase in energy consumption, which was affected by final use scale effect.
A number of key sectors contribute to a larger proportion of energy consumption in the city of Tangshan, including metal smelting and rolling processing (S14) which accounted for 53.2% of energy consumption in 2007, and increased 10.6% between 2007 and 2012. Results show that the four factors tested here contributed to −12.6% (technical effects), 11.2% (input structural effects), −25.2% (final use structural effects), and 37.3% (final use scale effect) increases in consumption between 2007 and 2012.
5. Conclusions and Policy Implications
In order to identify the factors contributing to changes in energy consumption in the city of Tangshan, we first analyzed current economic development and industrial structure within this region and then considered energy consumption. We used previously developed methods to determine the nature of factors affecting changes in energy consumption within the city of Tangshan. The four main conclusions of this research are summarized below.
From 2007 to 2012, the growth in GDP in the city of Tangshan was higher than the national average. Secondary industry output in Tangshan city accounts for an extremely high proportion of total GDP, much higher than the national average. This proportion translates into a rising trend, instead of a declining trend, in energy consumption during the studied period.
As a result of Tangshan’s economic development, Tangshan’s energy consumption in 2013 was nearly twice that in 2005. Coal and coke coal consumption was responsible for 96.2% of total energy consumption in 2005 and 95.1% in 2013, demonstrating that coal-related energy is the primary energy source, and that the energy consumption structure did not change significantly between 2005 and 2013.
In light of the increasing GDP and energy consumption, energy intensity has been gradually decreasing in Tangshan city. Tangshan’s energy intensity decreased from 3.00 tce/10 thousand Yuan in 2005 to 1.85 tce/10 thousand Yuan in 2013. However, the energy intensity of Tangshan was far greater than the average in China, and the rate of decrease in Tangshan’s energy intensity was much lower than China’s average.
In Tangshan city, the industries with the largest increases in energy consumption from 2007 to 2012 were metal products, construction, and metal smelting and rolling processing. Among the factors contributing to changes in energy consumption, the technical effect was the most important in decreasing energy consumption in most sectors, while the scale effect was the most important contributor to increases in energy consumption in all sectors. In contrast, the input structural and final use structural effects played different roles in different sectors.
In terms of policy, one obvious dilemma associated with reducing the energy consumption of the city of Tangshan is the dominance of heavy industry. Because the mining of metals, fabrication of metal products, and construction contribute the most overall to increasing energy consumption, any reduction brought about by technical changes to these industries will hardly offset corresponding increases caused by intermediate structural inputs and end-user consumption effects. Thus, strategies to significantly reduce energy consumption in the short term include adjustments to industrial structures and the development of low-energy-intensity industries. It is clear that strategies need to be implemented in policy in order to practically engage in the rationalization of industrial structures and modes-of-production. Improvements are required to both administrative and economic policies.
Our analysis of the effects of scale and technical factors show that, in terms of administrative policies, improving and enhancing the control of energy consumption standards as well as transforming the mode of economic growth to technique-oriented should be clear priorities if industrial production in the city of Tangshan is to be successfully adjusted. To do this, the government needs to strictly control the emissions of newly-added industrial producers in order to curb continued expansion of high energy consumption and high emissions, while at the same time, in terms of dealing with existing high energy consumption industries, we suggest that several typical enterprises are selected that exhibit the most energy saving potential so that available administrative resources are most effectively utilized in cutting energy consumption and in setting industry standards. It is also necessary that the government makes significant efforts to improve industrial production and techniques for the treatment of pollutants through investment and by encouraging research and development. Technical departments across all sectors would then be encouraged to try to solve these problems at their source. In addition, techniques involving administrative innovation also play equally important roles in steering reductions in energy consumption.
It is not sufficient or appropriate to simply rely on administrative means to reduce energy consumption as the market presently plays a much enhanced role. Thus, coordinating with administrative powers, the government of the city of Tangshan must adjust the local fiscal and taxation system and implement a number of other financial measures to directly lead and shape the development of industries. For example, the city government could levy high taxation on products from very heavily polluting and high energy consuming industries, while at the same time lowering taxation on enterprises that implement energy-conserving and emission-reducing production measures. In addition, the government could also waive administrative examination and approval fees for companies that replace traditional energy sources with renewable ones. The use of a range of different kinds of economic stimuli can move entire industries in the direction of energy conservation.