The growth of China's industry has been seriously depending on energy and environment. This paper attempts to apply the directional distance function and the Luenberger productivity index to measure the environmental efficiency, environmental total factor productivity, and its components at the level of subindustry in China over the period from 1999 to 2009 while considering energy consumption and emission of pollutants. This paper also empirically examines the determinants of efficiency and productivity change. The major findings are as follows. Firstly, the main sources of environmental inefficiency of China's industry are the inefficiency of gross industrial output value, the excessive energy consumption, and pollutant emissions. Secondly, the highest growth rate of environmental total factor productivity among the three industrial categories is manufacturing, followed by mining, and production and supply of electricity, gas, and water. Thirdly, foreign direct investment, capital-labor ratio, ownership structure, energy consumption structure, and environmental regulation have varying degrees of effects on the environmental efficiency and environmental total factor productivity.
Great achievements have been made in China’s economy during the past three decades of reform and opening up. However, with rapid economic growth, the depletion of natural resources and the environmental degradation have become increasingly prominent. Based on a forecast for 2005–2035, China is to replace the USA as the world’s leading embodied energy consumer in 2027, when its per capita energy consumption will be one quarter of that of the USA [
In much of the contemporary literature, researchers have been studying the changes in China’s efficiency and productivity and their influence on economic growth and transformation from various perspectives. Nevertheless, with increasingly prominent problems of resources and environment in the process of economic development, a growing number of researchers believe that resources and environment are not only endogenous variables, but also rigid constraints on economic development [
However, among these literatures, most of their data are based on subprovincial level in China, and very few of them are carried out from the subindustrial level in China. As Jorgenson and Stiroh [
Since traditional distance function cannot estimate the harmful effects of environmental pollution, many studies use indirect methods to calculate TFP with the consideration of pollutant emissions, which is obviously too simplified. Some researchers use radical and oriented data envelopment analysis (DEA) to compute directional distance function in order to simulate the harmful effects of environmental pollution, but this method will overestimate the efficiency of the evaluation object [
In addition, only one or several bad or undesirable outputs have been considered in the existing literature. However, for China’s industry at this stage, all energy inputs and pollutant emissions should be taken into account, by which environmental efficiency and environmental TFP can reflect the quality contribution to economic growth more precisely [
Therefore, on the basis of existing literature, this paper aims to use SBM directional distance function to measure environmental efficiency and its components of 36 subindustries of China’s industry, use SBM directional distance function and the Luenberger productivity index to measure the environmental TFP and its components, then test, and compare the differences of the determinants’ impacts.
Different from the traditional production function, production technology considering energy and environment must reflect resource saving and environmental protection. Since resources can be introduced into productivity analysis framework as traditional inputs (such as capital and labor), the difficulty of constructing production frontier function is how to take environmental factors into account. In order to combine energy and environment factors with traditional economic factors (capital, labor, and output), according to Färe et al. [
According to Fukuyama and Weber [
According to the existing literature, there are three main indexes to measure productivity: Malmquist index extended by Färe et al. [
According to Chambers et al. [
Following Grosskopf [
When the above five values are greater than 0, they, respectively, indicate the productivity improvement, efficiency improvement, technical progress, scale efficiency improvement, and technical deviation CRS, conversely reverses. While it is necessary to use eight directional distance functions to decompose Luenberger productivity index, four of them belong to CRS hypothesis, and the other four are estimated under the condition of VRS hypothesis.
industrial output is the most important good outputs, and it refers to gross industrial output value of 36 subindustries over the period from 1999 to 2009, which can be obtained from China Statistical Yearbook, published by National Bureau of Statistics of China (NBSC) [
considering the emissions of industrial pollutants, the bad or undesirable outputs should consist of industrial wastewater, carbon dioxide, sulfur dioxide, and solid waste. Emissions of wastewater, sulfur dioxide, and solid waste of each subindustry can be collected from NBSC. Unfortunately, there is no data of carbon dioxide emissions from NBSC, so this study follows Chen’s methods [
Here, the emissions of carbon dioxide are denoted by
Energy input should be considered as important as capital and labor. In the light of the majority of literature, this paper takes the number of employees every year as labor input and the energy consumption as energy input in subindustries, both of which can be inquired from NBSC [
Based on SBM directional distance function and Luenberger productivity index, this paper measures the environmental efficiency and environmental TFP by the software package Excel Solver Prem Platform V5.5 which is widely used in the present study. Environmental inefficiency values of every subindustry under the assumption of CRS and VRS are measured, respectively, and the results are given in Table
Environmental inefficiency and its components of China’s industry.
Category | Industry | Gross industrial output value | Capital stock | Number of employee | Energy consumption | Waste water emission | CO2 emission | SO2 emission | Solid waste emission | Total value |
---|---|---|---|---|---|---|---|---|---|---|
Mining | 0.195 | 0.051 | 0.012 | 0.119 | 0.057 | 0.07 | 0.076 | 0.083 | 0.663 | |
Manufacturing | 0.079 | 0.068 | 0.053 | 0.101 | 0.060 | 0.068 | 0.071 | 0.065 | 0.565 | |
VRS | Production and supply of electricity, gas, and water | 0.166 | 0.095 | 0.013 | 0.102 | 0.054 | 0.057 | 0.055 | 0.054 | 0.596 |
Mean value | 0.147 | 0.071 | 0.026 | 0.107 | 0.057 | 0.065 | 0.067 | 0.067 | 0.608 | |
| ||||||||||
Mining | 0.249 | 0.064 | 0.04 | 0.123 | 0.061 | 0.089 | 0.081 | 0.086 | 0.794 | |
Manufacturing | 0.124 | 0.088 | 0.075 | 0.126 | 0.067 | 0.085 | 0.085 | 0.081 | 0.734 | |
CRS | Production and supply of electricity, gas, and water | 0.257 | 0.128 | 0.023 | 0.145 | 0.08 | 0.085 | 0.073 | 0.076 | 0.867 |
Mean value | 0.209 | 0.093 | 0.046 | 0.131 | 0.069 | 0.086 | 0.079 | 0.081 | 0.798 |
Since the environmental efficiency value under VRS assumption does not consider scale efficiency, the value under VRS assumption must be lower than CRS assumption, which is confirmed in Table
The total value of environmental inefficiency of China’s industry is 60.8%. The main source of environmental inefficiency is the inefficiency of gross industrial output value (14.7%), followed by the inefficiency of energy consumption (10.7%), capital stock (7.1%), SO2 (6.7%), solid waste (6.7%), CO2 (6.5%), and wastewater (5.7%), and the inefficiency value of employee (2.6%) is far lower than other outputs and inputs. Therefore, the keys of improvement of environmental efficiency are growth of industrial output, energy saving and reduction of pollutant emissions.
In order to show the difference of environmental efficiency among subindustries due to industrial characteristics, this study classifies 36 two-digit code industries into three categories according to industrial classification standards provided by NBSC. The three industrial categories are mining, manufacturing, and production and supply of electricity, gas, and water. Table
The environmental TFP and its components are given in Table
Environmental TFP and its components of China’s industry.
Industry | LTFP | LPEC | LPTP | LSEC | LTPSC |
---|---|---|---|---|---|
Mining | |||||
Mining and washing of coal | 0.031 | −0.1199 | 0.0975 | 0.2151 | −0.1618 |
Extraction of petroleum and natural gas | 0.0027 | −0.2863 | 0.2561 | 0.0612 | −0.0283 |
Mining and processing of ferrous metal ores | 0.1022 | −0.1191 | 0.0561 | 0.1826 | −0.0175 |
Mining and processing of nonferrous metal ores | 0.1025 | −0.0137 | 0.061 | 0.0485 | 0.0067 |
Mining and processing of nonmetal ores | 0.0348 | 0.0044 | 0.0535 | −0.0242 | 0.0011 |
Mean | 0.0546 | −0.1069 | 0.1048 | 0.0966 | −0.0399 |
| |||||
Manufacturing | |||||
Processing of food from agricultural products | 0.0573 | −0.0075 | 0.0264 | 0.0781 | −0.0397 |
Manufacture of foods | 0.0529 | −0.0067 | 0.0586 | −0.0158 | 0.0167 |
Manufacture of beverages | 0.0425 | −0.0019 | 0.04 | 0.004 | 0.0005 |
Manufacture of tobacco | 0.0997 | 0.0294 | −0.0415 | 0.0139 | 0.0979 |
Manufacture of textile | 0.0227 | −0.012 | 0.0441 | −0.0183 |
|
Manufacture of textile wearing apparel, footware, and caps | 0.0247 | −0.0317 | 0.0408 | 0.0246 | −0.009 |
Manufacture of leather, fur, feather, and related products | 0.0089 | −0.0212 | 0.0209 | 0.0014 | 0.0078 |
Processing of timber, manufacture of wood, bamboo, rattan, palm, and straw products | 0.0222 | −0.0134 | 0.026 | 0.0082 | 0.0014 |
Manufacture of furniture |
|
−0.0173 |
|
0.0895 | 0.0978 |
Manufacture of paper and paper products | 0.0117 | −0.0004 | 0.0502 | −0.0963 | 0.0581 |
Printing and reproduction of recording media |
|
0.0289 | −0.0353 | 0.0168 | 0.0346 |
Manufacture of articles for culture, Education, and sport activities | 0.0558 | 0.0034 | −0.1198 | −0.0533 | 0.2254 |
Processing of petroleum, coking, and processing of nuclear fuel | 0.0104 | −0.0146 | 0.0444 | −0.0487 | 0.0293 |
Manufacture of raw chemical materials and chemical products | 0.0587 | −0.0035 | 0.0412 | 0.016 | 0.0051 |
Manufacture of medicines | 0.0272 | −0.0058 | 0.0182 | 0.0228 | −0.008 |
Manufacture of chemical fibers | 0.0187 | 0.0025 | 0.0216 | −0.0027 | −0.0026 |
Manufacture of rubber | 0.0213 | −0.009 | 0.0265 | 0.0034 | 0.0004 |
Manufacture of plastics | 0.0859 | −0.0656 | 0.058 | 0.054 | 0.0395 |
Manufacture of nonmetallic mineral products | 0.0755 | −0.0094 | 0.0943 | −0.0491 | 0.0398 |
Smelting and pressing of ferrous metals | 0.0893 | 0.0093 | 0.0534 | 0.0219 | 0.0047 |
Smelting and pressing of nonferrous metals | 0.0494 | −0.0038 | 0.038 | 0.0228 | −0.0076 |
Manufacture of metal products | 0.0219 | −0.0172 | 0.0287 | 0.0188 | −0.0084 |
Manufacture of general purpose machinery | 0.0783 | 0.0237 | 0.0532 | −0.0009 | 0.0023 |
Manufacture of special purpose machinery | 0.0516 | 0.0006 | 0.0579 | −0.0034 | −0.0035 |
Manufacture of transport equipment | 0.0457 | 0.0035 | 0.0354 | 0.0066 | 0.0002 |
Manufacture of electrical machinery and equipment | 0.0756 | −0.0399 | 0.0954 | 0.0123 | 0.0079 |
Manufacture of communication equipment, computers, and other electronic equipment | 0.1757 | 0.0014 | 0.0925 | 0.0534 | 0.0284 |
Manufacture of measuring instruments and machinery for cultural activity and office work | 0.1463 | 0.0332 | 0.0216 |
|
0.0014 |
Mean | 0.0558 | −0.0052 | 0.0288 | 0.0096 | 0.0225 |
| |||||
Production and supply of electricity, gas, and water | |||||
Production and supply of electric power and heat power | 0.0317 | 0.0144 | 0.0145 | −0.0574 | 0.0603 |
Production and supply of gas | 0.0703 | 0.0101 | 0.0176 | 0.0169 | 0.0257 |
Production and supply of water | −0.0273 |
|
0.0104 | −0.0195 | 0.0018 |
Mean | 0.0249 | 0.0015 | 0.0142 |
|
0.0293 |
| |||||
Total mean | 0.0451 | −0.0369 | 0.0493 | 0.0288 | 0.0039 |
The mean value of environmental TFP of mining is 5.46%, which is much higher than production and supply of electricity, gas, and water (2.49%), but lower than manufacturing (5.58%). Pure efficiency change and pure technical progress make the greatest contribution to the environmental TFP of mining; the mean values of which are 10.48% and 9.66%, respectively.
The main source of improvement of environmental TFP of manufacturing is pure technical progress and technical progress scale change; the mean values of which are 2.88% and 2.25%, respectively. Among the 28 subindustries of manufacturing, the value of environmental TFP of manufacture of communication equipment, computers and other electronic equipment (17.57%) is the highest, followed by manufacture of measuring instruments and machinery for cultural activity and office work (14.63%). Pure technical progress makes the greatest contribution to the former’s environmental TFP, while scale efficiency change makes the greatest contribution to the latter’s environmental TFP. The environmental TFP of some industries is very low, less than 2%, such as manufacture of leather, fur, feather and related products, manufacture of paper and paper products, processing of petroleum, coking, processing of nuclear fuel, and manufacture of chemical fibers, most of which are pollution-intensive industries.
Because of the negative value of scale efficiency change and low value of pure efficiency change, the mean value of environmental TFP of production and supply of electricity, gas, and water is much lower than that of manufacturing and mining.
What determines the environmental efficiency and environmental TFP of China’s industry? Loko and Diouf fully analysed the determinants of productivity growth [
Capital structure is denoted by the proportion of value-added of foreign direct investment (FDI) enterprises in the added value of industrial enterprises above designated size. China has received significant FDI inflows for the past three decades, and FDI has been an important factor influencing industrial efficiency and productivity growth. Zhou et al. pointed out that domestic firms in industries which have more FDI or have a longer history of FDI tend to have lower productivity [
Endowment structure is denoted by capital-labor ratio. Capital and labor are sources of comparative advantage for most industries. The rising of capital-labor ratio means capital deepening which is an important determinant of industrial efficiency and productivity growth. Empirical studies show that the elasticity of substitution between capital and labor is larger than the one in developed countries but smaller than that in developing countries [
Ownership structure is denoted by the proportion of added value of state-owned enterprises (SOEs) covering the total added value of industrial enterprises above designated size. At the outset of the transition towards a market economy, the governments in developing countries envisioned that privatization would be an efficient way to improve performance and productivity. The reform of state-owned enterprises has greatly affected the profitability and productivity of Chinese industrial firms [
Energy consumption structure is denoted by the proportion of electricity consumption accounted for total energy consumption. Different kinds of energy have different costs and pollution emissions which will influence the environment efficiency and environmental TFP.
China has adopted various policy measures to control industrial pollution. We need to assess the impact of pollution regulations on industrial productivity. Using the method of composite index, this paper builds a complex measurement system of China’s industrial intensity of environmental regulation. This system has a target layer (intensity of environmental regulation) and three evaluation layers (waste water, waste gas, and solid waste).
The main data sources are China Statistical Yearbook, China Energy Statistics Yearbook, Chinese Industry Economy Statistical Yearbook, and China Economic Census Yearbook published by NBSC [
The estimation results are given in Table
Estimation results of environmental efficiency and environmental TFPa.
Variable | Coefficient estimates | ||
---|---|---|---|
Predictor | Environmental efficiency | Environmental TFP | |
VRS | CRS | ||
Intercept | 3.393 (3.334)*** | 2.283 (2.209)*** | 2.561 (3.227)*** |
|
−7.234 (−6.789)*** | −7.583 (−7.968)*** | −8.821 (−10.271)*** |
| |||
|
0.16 (2.968)*** | 0.158 (1.260) | 0.172 (3.234)*** |
|
2.747 (0.994) | 2.969 (1.227) | 3.066 (3.485)*** |
|
15.031 (6.092)*** | 16.213 (6.256)*** | 15.615 (7.162)*** |
|
−0.157 (−2.314)** | −0.196 (−2.206)** | 0.034 (5.413)*** |
|
0.064 | 0.035 | 0.894 |
Observations | 396 | 396 | 360 |
aThe standard errors of coefficient estimates are in parentheses. ** and *** denote significance at 5% and 1% levels, respectively.
Capital structure (
The coefficients of capital labor ratio (
The coefficients of ownership structure (
The coefficients of energy consumption structure (
Environmental regulation intensity (
Using SBM directional distance function and Luenberger productivity index, this paper measures the environmental efficiency, environmental TFP, and its components at the level of subindustry in China over the period from 1999 to 2009 and tests the impacts of industrial capital structure, endowment structure, ownership structure, energy consumption structure, and intensity of environmental regulation. The findings of this study are crucial for environment administration and industrial upgrading. The specific policies are suggested as follows.
Considering the fact that both environmental efficiency and environmental TFP are different among subindustries, the government should accelerate the development of high-tech industries and environment friendly industries and limit the development of pollution-intensive industries and energy-intensive industries. The policy makers should also make vigorous guidance to draw FDI to high-tech industries and environmental-friendly industries and promote the industrial upgrading of FDI, protecting China from the pollution heaven of FDI.
Excessive energy consumption and pollutant emissions are the main sources of environmental inefficiency of China’s industry. Technological innovation makes significant contributions to the improvement of the environmental TFP of China’s industry. It is necessary to increase research investment to develop environmental technology, energy saving technology, low-carbon technology, and so on.
The government should make policies to promote industrial enterprises to reduce fossil energy consumption including coal and petroleum and improve the proportion of electricity consumption. It is also absolutely essential to vigorously develop clean energy such as nuclear, hydraulic, wind, and solar power.
The regulation strategies based on sectors are better than those based on provinces in terms of regulation costs [
This study has been supported by the National Nature Science Foundation of China (Grant nos. 71002086, 71203224) and the Fundamental Research Funds for the Central Universities (Grant no. 12JNQM010).