Abstract: Using directional distance function and nonparametric data envelopment analysis, this paper estimates the environmental total factor productivity (ETFP) of energy-intensive industries in China from 1995 to 2010, and performs an empirical analysis on factors affecting ETFP growth after studying the differences of energy-intensive industries’ ETFP by industries and provinces. The findings include the following: energy-intensive industries’ ETFP growth is mainly driven by technical progress; China, at its current development stage, still has the potential to raise the productivity of its energy-intensive industries. By estimating the provincial data, we find that the ETFP growth of different provinces converge at different levels. Further market liberalization, increased FDI flows and reductions in energy intensity will help to improve each province’s ETFP growth. In addition, increasing investment in energy saving and emissions reduction and improving corporate environmental management capacity can help to reduce a company’s short-term cost of complying with environmental regulations.
Keywords: energy-intensive industries, environmental pollution, environmental total factor productivity (TEFP), directional distance function
JEL: D24, O47, Q55
1. Introduction
Economic growth brings about evident environmental impacts. This is especially true in the case of China. As the fastest-growing economy in the world, China has become the second-largest economy with renowned economic achievements. However, China’s economic growth heavily depends on investment in resources instead of improvements in total factor productivity (TFP). According to the International Energy Agency (IEA), China’s energy consumption in 2000-2008 was four times greater than that during the 1990s. In 2009, China outstripped the U.S. to become the world’s largest energy consumer. Due to high energy consumption, China’s environment is coming under increasing pressures. Environment degradation, a consequence of rapid economic growth, may adversely impact current and future economic and social development in China (World Bank, 1997). People are now realizing that a growth model which sacrifices resources and the environment, and features heavy investment, high energy consumption and heavy pollution, is not sustainable.
Since the 11th Five-year Plan Period (2005-2010), high energy consumption and emissions of the industrial sector is even more alarming. The industrial sector contributes 40.1 percent towards China’s GDP in China, but consumes 67.9 percent of the country’s energy, and emits 83.1 percent of CO2 emissions. Besides, energy-intensive industries account for 80 percent of the industrial sector’s total emissions.1 The 12th Five-year Plan proposes cutting energy consumption per unit of GDP by 16 percent and CO2 emissions per unit of GDP by 17 percent during 2011-2015. These are not easy targets to achieve. Therefore, faced with the dual pressure of economic growth and improving the environment, many policymakers and scholars have shed much thought on the future of energy-intensive industries. However, existing studies rarely conduct research into these industries.
This paper attempts to analyze China’s energy-intensive industries from two aspects. First, we incorporate energy consumption and environmental pollution into the analytical framework by using the directional distance function and further breakdown China’s energy-intensive industries’ ETFP by industries and provinces. Second, we study the driving forces behind the energy-intensive industries’ ETFP growth at the provincial level by adopting a panel data model, so as to provide targeted suggestions for developing energy saving and emissions reduction policies during the 12th Five-year Plan period. This paper is structured as follows: Section 2 briefly introduces the analytical methods and sample data followed by reporting the calculation results of energy-intensive industries’ ETFP; Section 3 discusses provincial differences in the ETFP of energy-intensive industries; Section 4 performs an empirical analysis on factors affecting the ETFP growth of energy-intensive industries in China; and Section 5 provides conclusions and policy implications.
2. Calculation of Energy-intensive Industries’ ETFP in China
Following Solo’s study, TFP, a measure of technical progress, is regarded as an important driving force for sustained economic growth. Since China has encountered a number of difficulties during rapid economic development, calculations of TFP warrant much attention. Generally speaking, existing studies can be roughly divided into two categories. The first category studies TFP by regions or provinces and compares the TFP in different areas (Watanbe and Tanaka, 2007; Hu Angang et al., 2008; Wang Bing et al., 2010). The second category studies TFP by industries and analyzes each industry’s TFP growth and relevant factors (Chen Shiyi, 2010).
However, traditional studies tend to overlook environmental factors when calculating TFP, leading scholars to question the accuracy of TFP calculations and subsequent economic sustainable analysis based on these calculations. As energy supplies dwindle and environmental problems escalate, some scholars try to incorporate environmental factors into their productivity analyses in order to avoid the biases of leaving out the environmental factor or not calculating the factor in an appropriate way (Chung et al., 1997; Färe et al., 2001; Nanere et al., 2007). But when we use traditional approaches to calculate TFP with the environmental factor, we need to include the market prices of pollution, an undesirable output, which is difficult to measure2. Based on Shephard’s distance function, Chung et al. (1997) introduced the directional distance function (DDF) to model the joint production of desirable and undesirable outputs and used the Malmquist-Luenberger index (ML index) to measure TFP when pollution is present. This technique facilitates an increase in desirables while simultaneously reducing undesirables, namely pollution in this case. Moreover, this technique also boasts the advantage of the DDF which does not require pricing data. Obviously, such an approach is superior to former methodologies in terms of TFP calculation.
To incorporate environmental pollution into the analytical framework, in reference to Färe et al. (2007), we construct a production possibility set which includes both desirable (good) outputs and undesirable (bad) outputs and analyze ETFP using the DDF. Since the production possibility set includes pollution as a bad output, we term the set as environmental technology.
We assume that for each decision-making unit (DMU), the environmental technology is composed of three vectors: N types of input, as represented by vector ; M types of good output, as represented by vector , and I types of bad output, as represented by vector. The abovementioned input and good output are freely disposable3. Besides the standard axiom, the production possibility set, indicated by environmental technology, should also satisfy two additional axioms in order to incorporate the environmental factor into the analytical framework. The first axiom is the joint weak disposability of outputs, both good and bad. According to this axiom, pollution disposal involves a cost, which will divert resources from the production of good outputs and cause a reduction in the maximum attainable production of goods from a given endowment of resources. The second axiom is null-jointness, which means that good and bad outputs are jointly produced. In other words, if some good outputs are produced, a certain quantity of bad outputs must also be produced.
We define the directional vector of output expansion as g = (yt, -bt) allowing for increasing good outputs while simultaneously reducing bad outputs. The directional distance function with technology at time period t+1 and input and output at time period t can then be defined as follows:
Based on the directional distance function, we can now define the ML index. By deduction, we can prove that the ML index is equal to ETFP at constant returns to scale4. Chung et al. (1997) defined the ML index based on the directional distance functions of time period t and t+1 as follows:
The index can then be decomposed into two components, namely efficiency change and technical progress:
where MLEFCH denotes efficiency change, meaning the degree a DMU has moved towards the optimal production frontier; MLTECH is technical progress, indicating there is technical innovation in the DMU5. If the value of ML, MLEFFCH and MLTECH are greater than 1, signifying improvements in productivity, efficiency and technical progress, respectively; otherwise it signifies a decline in these aspects (Färe et al., 2001). The ML index is the product of efficiency change and technical progress, so it considers not only a DMU’s capacity of grasping existing technologies but also its endogenous creativity.
This paper calculates the TFP of China’s industrial sector using the abovementioned directional function. We include data from 38 two-digit industries in the industrial sector during 1996 to 2009 to construct panel data of the output and input while incorporating environmental pollution as a factor. Besides, we exclude data from the logging and transport subsectors of the timber industry before 2002 from the mining industry’s data and combine the data of waste resources and materials recovery and processing and the data of handicraft articles and other manufacturing under a single category ? other manufacturing. We also construct an input-output set which includes two types of output and three types of input. We use the gross industrial output value of each industry as the good output and the SO2 emissions of the industrial sector as the bad output. As for input, we include not only the traditional factors of capital and labor (represented by net value of fixed assets and total working population in the industrial sector, respectively), but also energy consumption as a third type of input (represented by each industry’s total energy consumption).
The time frame of this study spans from 1996 to 2009. However, during this period, the statistical coverage of industrial enterprises changed in 1998. Before 1998, the statistical coverage was based on industrial enterprises with independent accounting systems. In reference to the main economic indicators of industrial enterprises in China Economic Census Yearbook 2004, published in 2005, we calculated the ratio of industrial enterprises above a designated size (enterprises with annual sales revenue in excess of 5 million yuan) to all industrial enterprises covered in the census. With this ratio, we adjust the data of 1996 and 1997 to a unified statistical coverage, i.e. industrial enterprises above the designated size. We further convert gross industrial output value and net value of fixed assets into real amounts using 1996 as the base year and producers’ price indexes for manufactured products and price indices of investment in fixed assets as deflators. The amount of SO2 emissions of the industrial sector, total energy consumption and working population are provided in real terms, so there is no need to covert these data. The producers’ price indexes by industries are derived from the China Urban Life and Price Yearbook; other data sources include the China Statistical Yearbook, China Industrial Economic Statistical Yearbook and China Energy Statistical Yearbook.
We divide the 38 two-digit industries into groups according to energy intensity. According to our calculations, compared to medium and low energy-consuming industries, high energy consuming industries use more capital (2.9 times), consume more energy (7.5 times), deliver less output (0.8 times) and discharge much more pollutants (11.6 times)6. To illustrate, in 2009, high energy-consuming industries accounted for 62.5 percent of total investment utilized, 81.2 percent of energy consumption, 90.2 percent of SO2 emissions, and only 29.5 percent of gross industrial output.
Table 1 illustrates the year-on-year changes in total factor productivity, production efficiency and technical progress by different industrial groups in China from 1996 to 2009 with and without consideration of environmental pollution. According to our calculations, when energy consumption and environmental pollution are taken into account, an industrial sector’s average TFP grew by 9.88 percent annually, production efficiency declined by 1.35 percent, and technical progress advanced by 10.82 percent. This signifies that from 1996 to 2009, the industrial sector’s TFP growth is mainly driven by technical progress rather than production efficiency. Technical progress is relevant to the optimal production frontier while efficiency improvement is relevant to other changes in the production process, such as learning by doing and management efficiency improvements. The results also indicate that there is still much room for the industrial sector to improve its production efficiency. Our findings are consistent with those of Zheng and Hu (2006), Tu Zhengge (2008) and Chen Shiyi (2010), etc.
By comparing the ML index and the Malmquist index, we can see that the Malmquist index tends to overrate an industry’s TFP, because the former considers the environmental factor while the latter does not. In other words, when we leave out the environmental factor, we will get higher TFP for China’s industries. This is in line with our expectations. When environmental regulations are overlooked, an enterprise does not need to divert resources to reduce pollutants, so it can produce more with given resources. Nanere et al. (2007) pointed out that productivity might be overestimated when negative externalities (such as environmental pollution) are neglected; similarly, it might be underestimated when positive externalities are left out.
We also find that the ETFP growth of different industries varies significantly; for example, coal mining and dressing registers a growth rate of 0.1 percent while that of the communications equipment manufacturing reaches 29.7 percent. The most efficient enterprises are in IT or Hi-tech industries while the most inefficient ones are in heavy chemical industries. By comparing high energy consumers with medium and low ones, it is clear that high energy consumers have much lower annual growth rates and technical progress rates. These two indexes of high energy consumers are 5.8 percent and 6.7 percent, respectively while those of the other group are 18.9 percent and 19.1 percent, respectively. Among high energy consumers, the chemical fiber manufacturing industry has the highest ETFP (21.4 percent), which, we believe, is the result of introducing high-end equipment and stepping up efforts in research and development in recent years. However, most industries with low ETFP among high energy consumers come from the heavy chemical industries, such as coal, oil and gas mining; metal and non metal mining and manufacturing; raw chemical materials and chemical products manufacturing; and electric power and gas production.
The ETFP of high energy consuming industries drops considerably when the environmental factor is considered. Such a decline seems to be contradict the rapid output growth of the these industries (cumulative growth of output is 6.5 times). Analyzing data in Table 2 will help us to better understand this contradiction. First, from 1996 to 2009, industrial output in China grew by 8.4 times while ETFP only increased by 2.4 times. ETFP’s contribution to the sector’s gross industrial output stands at approximately 36.4 percent. Second, ETFP that incorporates an environmental factor is significantly lower than the traditional TFP that does not consider an environmental factor. In comparison to low energy consumers, high energy consumers have a much lower cumulative growth of output and ETFP. The difference in ETFP is more pronounced (for example, the output growth rate is 12.6 times for the low energy consumers group and only 6.5 times for the high energy consumers group; the ETFP growth rate for the former is 8.5 times while it is only 1.1 times for the latter). Similarly, higher energy consumers’ ETFP contribution share (27.8 percent) is much less than the lower ones’ (70.3 percent). The ETFP of high energy consumers are significantly lower than that of low energy consumers and the sector’s average. That means even though the growth rate of high energy consumers group is high, but their overall productivity remains low. Furthermore, the growth of these enterprises are driven by resource inputs rather than ETFP improvements. Such a growth model merits pondering. If the current model persists, the pressure will mount for China to save energy, reduce emissions and better manage the environment, which will further threaten the sustainable growth of the economy and bring about much larger environmental and economic costs. Therefore, high energy consuming industries must accelerate their productivity levels through equipment retrofits and technological advancements so as to ensure more sustainable and orderly growth.
3. ETFP of Energy-intensive Industries at the Provincial Level and Its Variance Analysis
After calculating the ETFP of energy-intensive industries, we formed an initial understanding of its basic conditions. However, due to significant geographical differences and resource endowments among provinces and autonomous regions, the development of energy-intensive industries in different regions also varies. In order to promote energy conservation and optimization of the industrial structure, the following sections will carry out in-depth analysis of ETFP at the regional level.
We selected energy-intensive industries throughout China’s 30 provinces, autonomous regions and municipalities8 as the object; the timeframe spans from 1998 to 2008. As usual, we constructed input-output panel data9 with two outputs and three inputs. Industrial merger and various indicators are converted into actual values using 1998 as the base period. In accordance with the China Statistical Yearbook of 2006, we divided China’s 30 provinces (regions) into eastern, central and western regions and provided the Malmquist-Luenberger (ML) productivity index and Malmquist index for each region. Table 3 provides the ML index, efficiency variation and technical progress index of all 30 provinces (regions) after considering environmental pollution between 1998 and 2008. Table 3 also suggests that the ML index which considers environmental pollution is lower than the Malmquist index which does not; in other words, after taking into account the negative externality of output, regional TFP significantly decreases, which is consistent with the findings of Watanbe and Tanaka (2007) and Hu Angang (2008). It is worth noting that the energy-intensive industries’ ML index showed a greater heterogeneity at the inter-provincial level, for example from 0.9242 (Chongqing) to 1.2477 (Tianjin).
Overall, between 1998 and 2008, the average annual growth rate of energy-intensive industries’ ETFP reached 10.9 percent, the productivity index decreased by 0.71 percent, and the technological progress index increased by 13.02 percent. This shows that energy-intensive industry’s ETFP at the inter-provincial level is mainly driven by technological progress, with less contribution coming from efficiency improvements, which is more consistent with the results of Watanbe and Tanaka (2007). In comparison, the eastern region registered the highest ETFP annual average growth rate (12.7 percent), followed by the central region (2.7 percent), and the western region (-0.05 percent); provinces with more than a 10 percent ETFP average annual growth rate are located in the eastern region, such as Jiangsu (20.1 percent), Shanghai (19.3 percent), Beijing (18.9 percent) and Zhejiang (11.1 percent) while provinces with negative ETFP average annual growth rates are mostly located in the western region, such as Guangxi (-0.6 percent) Guizhou (-1.8 percent) and Chongqing (-7.6 percent). A decomposition analysis of the ML index further reveals that the western region is not only insufficient in terms of technological innovation, but also lags behind in efficiency improvement; this may be correlated with extensive local growth patterns.
To further examine the growth mode of energy-intensive industries between various regions and ETFP’s contribution to output growth, we further analyzed the data listed in Table 4. During 1998-2008, the output of energy-intensive industries increased by 5.98 times, ETFP increased by 1.4 times, and ETFP’s contribution rate stood at 34.8 percent. Similar to industry analysis, traditional technology tends to overestimate a region’s ETFP contribution rate. Through comparison, it is easy to conclude that output and ETFP growth in the eastern region are significantly higher than those found in the central and western regions. ETFP contributes 40.6 percent in eastern region and 23.4 percent in central region and 15.1 percent in western region. Among the western region’s energy-intensive industries, output growth increased by 5.6 times while ETFP appeared to experience negative growth. This suggests that in the western region, output growth is inefficient and growth is mainly driven by factor inputs. The western region is relatively rich in coal and oil resources; however, the above analysis suggests that avoiding the so-called “resource curse” and enhancing the power of human capital and R&D will be a challenge for the western region’s long-term economic growth. In addition, relatively rich resource endowments may also weaken the effectiveness of the political system, which would weaken the contribution of productivity growth. Mauro’s (1998)’s shows that rich resources may incubate political interest groups, breeding rent-seeking and corruption, thus weakening the effectiveness of the political system, and generating a negative nonlinear effect on economic growth as referenced by Sala-i-Martin and Subramanian (2003).
4. Empirical Test of Factors Affecting the ETFP Growth of Energy-intensive Industries
TFP is not the only factor which determines economic growth and residents" welfare, but it does provide us an important means to measure a country’s economic prosperity, living standards and the competitiveness of its enterprises. In order to analyze factors that affecting ETFP, we build the following panel data regression model by drawing on existing literature (Lall et al., 2002; Watanbe and Tanaka, 2007) and encompassing the basic characteristics of China’s energy-intensive industries.
(4)
Where, dependent variable ETFP denotes Environmental Total Factor Productivity10; αi and βi denote estimated parameters; α0 denotes intercept; εit denotes residual items, including individual effect and random perturbation. Ln (K / L) is the logarithm of the capital-labor ratio, i.e. capital per person, used to measure the endowment structure of energy-intensive industries; Lageff denotes the productivity of lag I, used to measure technical efficiency in the early phase; other explanatory variables X in the model include: (1) energy consumption per unit of industrial output denotes energy intensity (Ei); (2) the degree of market-oriented reforms Market or the proportion of total industrial output value of non-state-owned enterprises; (3) the intensity of foreign direct investment (FDI) as presented by the ratio of FDI in total industrial output value is applied to foreign capital inflow’s impact on ETFP, as well as to validate the “pollution haven” hypothesis; (4) R&D intensity is measured by the proportion of R&D expenditures in total industrial output; (5) Expenditure in energy-saving investments (Exp) is depicted by the amount of completed investment in pollution controls to total industrial output, which is used to measure intensity of investment in energy-saving and emissions control; (6) Corporate environmental management capacity (Remove) is depicted by the removal rate of industrial SO2 (the sum of removal amount and emissions amount divided by the removal amount).
Table 5 provides the regression results of the model. The Hausman test shows that while performing regression analysis, a fixed effect should be chosen. However, due to the existence of heteroscedasticity and autocorrelation in panel data, we correct the model with feasible generalized least squares (FGLS). Through comparison, we find the three methods are more consistent with the regression results of the main explanatory variables; subsequently, the model results are more robust and the explanatory variables have a similar impact on ETFP, with the only exception being that FGLS results are more significant.
Data in Table 5 suggests that the negative impact of capital intensity over ETFP signifies diminishing marginal output. The negative correlation between the technical efficiency level of lag I and ETFP suggests a lower ETFP growth in areas closer to the forefront of environmental technology. This result also reflects the “catch-up effect” of the latter (Lall et al, 2002) to verify the convergence between different regions. Energy intensity and ETFP are negatively correlated at the 1 percent significance level, indicating that the low efficiency of energy use is a key factor which hinders ETFP growth. The regression results further show the significant positive role marketization plays in ETFP.
At this stage, energy-intensive industries are mainly represented by state-owned enterprises; hence, deepening the reform to introduce corporate and shareholding systems in large state-owned enterprises and nurturing a vibrant market economic system will be effective measures promoting ETFP growth in energy-intensive industries. FDI has a positive correlation with the ETFP growth of energy-intensive industries at the 1 percent significance level, indicating that foreign capital inflow is conducive to productivity growth. This may be due to the fact that most energy-intensive industries include heavy chemical enterprises, with the introduction of foreign advanced technology and equipment through FDI facilitating technological spillover and thereby promoting ETFP growth. However, this result does not support the “pollution haven” hypothesis11 that FDI inflows aggravate environmental pollution.
Regression results in Table 5 suggest that R&D has a negative effect on ETFP, which is different from our expectations; however, this result may also indicate that some problems exist in current energy-intensive industries’ R&D activity. Similar to Li Xiaoping’s (2008) point of view, we believe that this may be related to institutional factors. Under the imperfect state-owned management structure, there may be serious soft budget constraints and problems of “agency by mandate”, i.e. investment in R&D from state-owned enterprises may be more focused on “political projects” which bring short-term benefits but lack long-term returns (the significant positive correlation of marketization also verified the low efficiency of state-owned energy-intensive industries). Despite an insignificant regression coefficient of the corporate environmental management capacity, its positive correlation with ETFP also suggests that corporate environmental management has a positive role in raising the ETFP growth rate; hence, the design of environmental regulation policies should seek to take advantage of enterprises’ initiatives, and effectively improve corporate environmental management capacity. An increase of investment in energy saving at the 5 percent significance level promotes ETFP growth, indicating that in the long term, energy saving policies will facilitate the positive transformation of energy-intensive industries, and also promote production efficiency and technological improvement. Compared with the other explanatory variables, for each additional 1 percent rise in energy conservation expenses, ETFP will rise by 7.7 percent. According to the “innovative compensation” approach outlined in the environmental Porter Hypothesis, high energy consumption and high pollution levels are actually a signal of invalid resource use. From a long-term point of view, fundamentally sound environmental policy coupled with incentive and restraint mechanisms will stimulate technological innovation, increase productivity and offset the short-term impact of environmental regulatory policies, ? even produce additional net income for manufacturers ? therefore achieving a win-win situation of saving energy and improving productivity.
5. Conclusions
Despite some progress in energy saving and emissions control during China’s 11th Five-Year Plan period (2006-2010), resource and environmental costs as byproducts of rapid economic growth have yet to be significantly alleviated. Instead, pressure from resource protection and environmental management is on the rise. According to “China’s Environmental and Economic Accounting 2008 (Public Edition)”, the cost of environmental degradation in the Eleventh Five-Year Plan period likely increased from 511.82 billion yuan to 894.76 billion yuan, an increase of 74.8 percent. The 12th Five-Year Plan (2011-2015) proposes more stringent energy saving and emissions control objectives, and stresses the need to strengthen supervision and rectification on energy-guzzling and heavy-polluting industries. Since emissions from energy-intensive industries account for 80 percent of the total emissions from the industrial sector, under the dual pressure of promoting economic growth and improving environmental quality, increased oversight over energy-intensive industries poses a difficult task for local governments.
Analyzing the ETFP of energy-intensive industries provides us with a better angle to understand the complex relationship between economic growth and sustainable development. Correctly calculating ETFP will also help to recognize the effectiveness of current environmental and economic policies, which equips policymakers with more realistic and effective information.
This paper estimates the ETFP of China’s energy-intensive industries from the Ninth Five-Year Plan period (1996-2000) to the 11th Five-Year Plan period (2006-2010) using the directional distance function approach. An empirical analysis was conducted on the affecting factors after surveying industrial and inter-provincial differences. The results of this study show that after considering energy and environmental factors, ETFP is significantly lower than TFP based on traditional calculation methods, signifying that the traditional TFP index may overestimate the TFP of energy-intensive industries. Therefore, analyzing the productivity of energy-intensive industries should refer to the calculation results of the ETFP. This paper further decomposes the TFP to reveal that regardless whether one looks at it from the industrial or regional level, the ETFP growth of energy-intensive industries is mainly driven by technological progress; however, this process is still hindered by low productivity overall, indicating that a large potential still exists for improving the production efficiency of energy-intensive industries.
Calculations at the industrial level reveal that the average annual growth rate of ETFP in energy-intensive industries is only 5.8 percent, well below the 18.9 percent of the low energy-consuming industries This result largely confirms the basic features of energy-intensive industries, which are rapid growth and low efficiency; in other words, the productivity growth of energy-intensive industries is mainly driven by inputs, rather than TFP-driven. Further ETFP calculations at the provincial level reveal a convergence characteristic for ETFP among provinces and autonomous regions. ETFP grows faster in provinces which are distant from the forefront of environmental technology, indicating that the spillover effects of technology and knowledge will make regional convergence possible. Empirical test of ETFP growth factors of energy-intensive industries further suggests that market-oriented reforms, FDI inflows as well as declines in energy intensity are conducive to the promotion of provincial ETFP growth. However, investment in energy saving and emissions control, coupled with improvement in environmental management capacity can reduce the impact of control policies on enterprises’ short-term costs. The results suggest that future energy saving policies should be more economic incentive-based policy tools to improve stakeholder enthusiasm for environmental management initiatives, thereby reducing the implementation costs of environmental policies.
Some policy implications regarding the management of energy-intensive industries can be drawn from this study: Despite a relatively high ETFP among the eastern region’s energy-intensive industries, the eastern region still accounts for a majority of China’s resource consumption. Are promoting technological innovation and industrial upgrading in the eastern regions the only ways in which we can substantially reduce pollution emission in the region and thereby improve the overall emissions reduction efficiency of energy-intensive industries? Regional industrial concentration and energy-saving investments are conducive to the gradual improvement of ETFP in energy-intensive industries. Central and western regions are gradually receiving the relocation of energy-intensive industries from the eastern region; hence, strengthening policy guidance for energy-intensive industries is significant for promoting energy conservation in central and western regions. Considering the strong inter-provincial heterogeneity of emission models in energy-intensive industries, policy-making should have greater regional relevance and flexibility. During the 12th Five-Year Plan period, China should design new mechanisms to ensure that governments at different levels can change the current mode of economic growth and patterns of thought, from passively meeting the requirements to actively engaging in energy-saving and emissions reduction initiatives; secondly, optimize the industrial structure, to enhance the ability of technological innovation to increase energy efficiency, and to develop alternative clean energy sources. Finally, by improving regional transfer payments between the central and local governments so that all levels of government can more effectively engage in energy saving and emissions reduction initiatives, China’s energy-intensive industries will then be able to embark on the road of sustainable development, curbing pollution and enhancing the people’s overall quality of life.
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