Chapter 3 Research Design
3.2 Abatement Evaluation
Labor and capital are two major inputs in production. When measuring a nation’s overall output, GDP is commonly used. For example, Färe et al. (1994) analyzed the productivity growth of OECD countries, by considering labor and capital as inputs and GDP as an output. Chang and Luh (2000) adopted similar inputs and outputs to analyze the productivity growth of ten Asian economies.
The change in income, energy, and waste is a three-way relation: First, increasing income deteriorates the environmental condition directly, because waste is generally a by-product of the energy consumption and is costly to dispose. Conversely, the growth of income is accompanied by the public increasing its demand for better environmental quality through driving forces such as control measures, technological progress, and the structural change of consumption. GDP, waste, and energy should be both taken into account in order to correct a nation’s output. Therefore, in the following analytical process, energy and waste are considered respectively as inputs in order to find the target input levels by the DEA approach.
Input Factors
However, the crude oil can be divided into liquefied petroleum gas, gasoline, light oil, diesel oil, heavy oil, and pitch, etc. after distilling. Besides petrol, the others can be used for generating electricity. In order to avoid repeated calculation on energy consumption, this thesis selects gasoline as an input variable. Therefore, this thesis takes the economic production function that is constructed by data envelopment analysis to analyze regional efficiencies in China. First, three major types of energy (coal, gasoline, and electricity) are considered in conjunction with the inputs of labor and capital stock (that are normally used in economic efficiency and productivity analysis) as the total inputs in order to produce economic output (GDP). Second, this thesis treats three kinds of waste (solid wastes, waste water, and waste gas) as proxies for the cost of environmental goods used for production. Oates and Schwab (1988), López (1994), Smulders (1999), de Bruyn (2000), and Hu et al. (2005) treated the pollution as a proxy for the cost of environmental goods. Three kinds of waste therefore are considered in conjunction with the inputs of labor and capital stock as the total inputs in order to produce economic output (GDP). Figure 2 shows the production model for evaluating efficient abatements.
Figure 2 Production model for evaluating efficient abatements
The target inputs and outputs for a DMU to be efficient can be computed by the DEA approach. The efficiency frontier can shift from year to year. DEA calculates the year-specific frontier with regional output and input (cross-sectional) data in each year. The target inputs of a DMU for a year are found by comparing its actual inputs to the efficiency frontier in that year. By this method, each region’s target amounts of coal consumption, gasoline consumption, electricity consumption, solid wastes produced, waste water discharged, and waste gas emission in each year can be found by comparing their actual consumption to the total-factor efficiency frontier of that year - that is, the efficiency frontier in each year represents the feasible and best performance of China in that year. Therefore, an imposition of an arbitrary abatement target with a developed economy’s standard is avoided herein.
Hu (2006) constructed a total-factor air pollution abatement ratio index to compute how far away a region’s air pollution emission is from the efficient level.
The higher the abatement ratio is, the lower the total-factor efficiency will be. The target input (energy or waste) abatements ratios of the regions are then computed from dividing the target input amount by the actual input amount:
Target Input Abatement Ratio k (i, t) = 1 - Target Input k (i, t) / Actual Input k (i, t), (2)
where it is in the i-th region and the t-th year for k-th input. Different from the traditional DEA model which emphasized efficiency, this thesis creates an input abatement index. It is main contribution of this thesis too. As equation (2) shows, the abatement ratio represents how far away a region’s three major types of energy or waste are from the efficient levels. Therefore, the efficient target energy savings ratios for each region in each year are then obtained by dividing the target energy consumption by the actual energy consumption; and the efficient target waste abatements ratios for each region in each year are then obtained by dividing the target
waste and by the actual waste. The actual value is always larger than or equal to the target value such that the abatements ratios will always be between zero and unity.
The DEA approach was originally intended for use in microeconomic environments to measure the performance of schools, hospitals, and the like, and it is also ideally suited to macroeconomic performance analysis. However, to the best our knowledge, the existing literature of efficient energy target savings ratios and efficient waste target abatements ratios do not simultaneously incorporate the various energy and waste. For example, Hu (2006) uses three air emissions as inputs to compute the efficient air pollution abatement ratios in China. Hu and Lee (Forthcoming) find the target waste abatement of the three wastes for twenty-seven regions in China through the DEA. Färe et al. (2004) used DEA to construct an environmental performance index focusing on pollution.
3.3 Data Sources
The data of regional labor employment are established from the China Statistical Yearbook. Data of GDP output in each region are collected respectively as stated previously. Real capital stocks in 1996 prices are constructed based on Li’s method (Li 2003).1 Monetary inputs and outputs such as GDP and capital stock are deflated to 1996 values.
From the perspective of China’s development and political factors, its provinces, autonomous regions, and municipalities are usually divided into three major areas: the east area (abbreviated as ‘E’), the central area (abbreviated as ‘C’), and the west area (abbreviated as ‘W’). There is an apparently economic disparity between the coastal
1 The capital stock data are not available in the China Statistical Yearbook. In this study, every regional capital stock in a specific year is calculated by the authors according to this formula: capital stock in the previous year + capital formation in the current year − capital depreciation in the current year.
All the nominal values are deflated in 1995 prices before summations and deductions. This thesis finds
and inland areas. Regional economic disparities are due to greater access to world markets, better infrastructure, a higher-educated labor force, and the government's preferential policies on foreign investment for the east area (World Bank 1997).
From China Energy Statistical Yearbook, we establish the three types of energy dataset for 27 regions in mainland China (24 provinces and 3 municipalities) during 2000 to 2003. There are Beijing (E), Tianjin (E), Hebei (E), Shanxi (C), Inner Mongolia (C), Liaoning (E), Jilin (C), Heilongjiang (C), Shanghai (E), Jiangsu (E), Zhejiang (E), Anhui (C), Fujian (E), Jiangxi (C), Shandong (E), Hennan (C), Hubei (C), Hunan (C), Guangdong (E), Guangxi (E), Sichuan (W), Guizhou (W), Yunnan (W), Shaanxi (W), Gansu (W), Qinghai (W), and Xinjiang (W). From China Statistical Yearbook, we establish the three types of waste dataset for 27 regions in mainland China (24 provinces and 3 municipalities) during 2000 to 2003. Note that Chongqing became a municipality out of Sichuan in 1997 and in this study its outputs and inputs are included in Sichuan.
There are five inputs and one output in the DEA model to calculate the target energy savings. In order to avoid repeated calculation, this paper only regarded final consumptions as energy inputs. The five inputs are capital stock, number of employed persons, coal consumption, gasoline consumption, and electricity consumption. There are also five inputs and one output in the DEA model to calculate the target waste abatements. The five inputs are capital stock, number of employed persons, industrial solid wastes produced, industrial waste water discharged, and industrial waste gas emission. The only one output is GDP of a specific region. These include aggregated input and output proxies. Three inputs of energy and three inputs of waste are treated as the cost of production, and they are China’s three types of most important energy and waste. The values of monetary inputs and outputs such as GDP and capital are in 1996 prices. This thesis uses the software Deap 2.1, kindly provided by Coelli (1996), for computing the target inputs and outputs of each region in each year.
3.4 Descriptive Statistics
Summary statistics of these inputs and output ordered by year and area are shown in Tables 7 and 8, respectively.
Table 7 Summary statistics of inputs and outputs by year
2000 2001 2002 2003
Output
Gross Domestic Product Mean 2623 2687 2728 2848 (100 million RMB) Std. Dev. 1833 1889 1935 2055
Common Inputs
Capital Stock Mean 11647 12366 13105 13943 (100 million RMB) Std. Dev. 8607 8979 9376 9853
Number of Employed Persons Mean 2305 2291 2334 2374 (10,000 persons) Std. Dev. 1535 1536 1522 1540
Energy Inputs
Volume of Coal Consumption Mean 5396 5624 6166 7077 (10,000 tons) Std. Dev. 3311 3487 4077 4588
Volume of Gasoline Consumption Mean 125 136 144 158
(10,000 tons) Std. Dev. 64 76 81 93
Volume of Electricity Consumption Mean 501 565 611 699 (100 million KWH) Std. Dev. 296 366 377 448
Waste Inputs
Volume of Industrial Solid Wastes Produced Mean 3001 3271 3479 3687 (10,000 tons) Std. Dev. 2090 2253 2326 2439
Volume of Industrial Waste Water Discharged Mean 71237 74350 76004 77926 (10,000 tons) Std. Dev. 53685 61966 62891 62839
Volume of Industrial Waste Gas Emission Mean 5046 5890 6411 7283 (100 million m3) Std. Dev. 2892 3597 3830 4395 Notes: (1) The monetary values are in 1996 prices.
(2) Data source: China Energy Statistical Yearbook 2004-2005, and China Statistical Yearbook 2001-2004.
Table 8 Summary statistics of inputs and outputs by area
Area of Mainland China
East Central West Output
Gross Domestic Product Mean 4002 2196 1385 (100 million RMB) Std. Dev. 2070 910 1258
Common Inputs
Capital Stock Mean 19616 8885 6989 (100 million RMB) Std. Dev. 9734 3297 5817
Number of Employed Mean 2404 2430 2070 (10,000 persons) Std. Dev. 1395 1418 1806
Energy Inputs
Volume of Coal Consumption Mean 6580 7145 3870 (10,000 tons) Std. Dev. 3973 4155 2392
Volume of Gasoline Consumption Mean 180 126 98
(10,000 tons) Std. Dev. 77 71 63
Volume of Electricity Consumption Mean 820 471 396 (100 million KWH) Std. Dev. 442 194 245
Waste Inputs
Volume of Industrial Solid Wastes Produced Mean 3572 3737 2541 (10,000 tons) Std. Dev. 2686 1897 1783
Volume of Industrial Waste Water Discharged Mean 102217 63640 46370 (10,000 tons) Std. Dev. 62448 34449 64264
Volume of Industrial Waste Gas Emission Mean 8262 5625 3535
(100 million m3) Std. Dev. 4164 2618 2158
Notes:
(1) The monetary values are in 1996 prices.
(2) Data source: China Energy Statistical Yearbook 2004-2005, and China Statistical Yearbook 2001-2004.
The east area has the highest GDP, capital stock, gasoline consumption, electricity consumption, and. the most waste water discharged and waste gas emission The central area has the largest number of employed people, the highest coal consumption, and the most solid wastes.
As shown in Table 9, all inputs have positive correlation coefficients with the output - that is, all inputs satisfy the isotonicity property with the output.
Table 9 Correlation coefficients between inputs and the output
Part I: Single output, Common inputs, and energy inputs
Real Capital Stock Labor Coal Gasoline Electricity Real GDP 0.81 0.68 0.47 0.81 0.93 Part II: Single output, Common inputs, and waste inputs
Real Capital Stock Labor Solid Wastes Waste Water Waste Gas Real GDP 0.81 0.68 0.82 0.79 0.29
4. Empirical Analysis
This section is divided into two parts: Part 4.1 presents empirical results of mainland China’s energy; and part 4.2 shows the empirical results of mainland China’s waste.
4.1 Energy of Mainland China
4.1.1 Regional Savings Ratios for Three Major Types of Energy
After the DEA computation, this part shows the regional target savings ratios of coal consumption, gasoline, and electricity consumption during 2000-2003, and the average regional target savings ratios of coal, gasoline, and electricity during 2000-2003.
Figures show the trends of regional target savings ratios in the three major areas of mainland China.
4.1.1.1 Savings Ratios for Coal Consumption
As Table 10, the east area has two regions with coal consumption savings ratios always higher than 30% throughout the research period: Tianjin (02) and Liaoning (06). The central area has six regions with coal consumption target savings ratios always higher than 30%: Shanxi (04), Inner Mongolia (05), Jilin (07), Heilongjiang (08) and Hubei (17), especially Shanxi (04) and Inner Mongolia (05) with target savings ratios higher than 80%. The west area has five regions with coal consumption target savings ratios always higher than 30%: Guizhou (22), Yunnan (23), Shaanxi (24), Gansu (25), Qinghai (26), and Xinjiang (27), especially Guizhou (22) and Gansu (25) with target savings ratios higher than 60%.
Table 10 Actual consumption and target savings ratios of coal for regions in Mainland
Notes: 1. Actual consumption is in 10,000 tons.
2. Savings ratios are in percentage terms.
Table 10 and Figure 3 describe the 2000-2003 average coal consumption savings ratios in each area. The coal consumption savings ratios of the east area are the lowest, and which of the central is the highest With respect to coal consumption, the east, central, and west areas are the most, medium, and least efficient, respectively. Among the three major types of energy, the coal consumption target savings ratios are generally the highest, implying that coal consumption may be the most critical task for saving energy in mainland China.
Coal
0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00
2000 2001 2002 2003 year
%
E C W
Figure 3 The average target of coal savings ratios in the three major areas of Mainland China
Table 11 shows the Mann-Whitney test on target coal savings ratios for three major areas in mainland China during 2000-2003. It presents that the target savings ratios of west area is always significantly more than east area, which of the west area is not significantly more than central area; and which of central area is not significantly more than east area. Summary of target coal savings for three areas, east area is most efficient, and west area and central area are more inefficient.
Table 11 Mann-Whitney test on target coal savings ratios for three major areas
Note: ***represents significance at the 0.01 level.
4.1.1.2 Savings Ratios for Gasoline Consumption
The east area has two regions with gasoline consumption target savings ratios always higher than 30% throughout the research period: Beijing (01) and Tianjin (02).
The central area has two regions with gasoline consumption target savings ratios always
gasoline consumption target savings ratios always higher than 30%: Guizhou (22), Shaanxi (24), Gansu (25), Qinghai (26), and Xinjiang (27), especially Gansu (25) with target savings ratios higher than 60%.
Table 12 and Figure 4 show the 2000-2003 average gasoline consumption target savings ratios in each area. The east, central, and west areas have the lowest, medium, and highest gasoline consumption savings ratios, respectively. With respect to gasoline consumption, the east, central, and west areas are the most, medium, and least efficient, respectively.
Average gasoline consumption target savings ratios of east and west areas are stable throughout the research period, which of central area are decreasing. However, the average target savings ratios of the west area always stayed above 40% during the 2000-2003 period, showing no significant improvement at all.
Table 13 shows the Mann-Whitney test on target gasoline savings ratios for three major areas in mainland China during 2000-2003. It presents that the target savings ratios of west area is significantly more than east area, which of west area is significantly more than central area; and which of the central area is not significantly more than east area. Summary of target gasoline savings for three areas, east area and central are more efficient, and west area is most inefficient.
Table 12 Actual consumption and target savings ratios of gasoline for regions in mainland China during 2000-2003
2000 2001 2002 2003
ID Region Area Actual
Consumption
Notes: 1. Actual consumption is in 10,000 tons.
2. Savings ratios are in percentage terms.
Table 13 Mann-Whitney test on target gasoline savings ratios for three major areas
Note: ***represents significance at the 0.01 level; **represents significance at the 0.05 level;
*represents significance at the 0.1 level.
Gasoline Oil
0.00 10.00 20.00 30.00 40.00 50.00
2000 2001 2002 2003 year
%
E C W
Figure 4 The average target of gasoline savings ratios in the three major areas of mainland China
4.1.1.3 Savings Ratios for Electricity Consumption
As Table 14, the east area has one region with electricity consumption target savings ratios always higher than 20% throughout the research period: Liaoning (06).
The central area has two regions electricity consumption target savings ratios always higher than 30%: Shanxi (04) and Inner Mongolia (05), especially Shanxi (04) with target savings ratios higher than 50%. The west area has four regions with electricity consumption target savings ratios always higher than 30%: Guizhou (22), Shaanxi (24), Gansu (25), and Qinghai (26), especially Guizhou (22) and Qinghai (26) with target savings ratios higher than 60%.
Figure 5 show the 2000-2003 average electricity consumption savings ratios in each area. The west area always had higher target savings ratios than others. With respect to electricity consumption, the east, central, and west areas are the most, medium, and least efficient, respectively.
Table 14 Actual consumption and target savings ratios of electricity for regions in mainland China during 2000-2003.
2000 2001 2002 2003
ID Region Area Actual
Consumption
Notes: 1. Actual consumption is in 100 million KWH.
2. Savings ratios are in percentage terms.
Electricity
0.00 10.00 20.00 30.00 40.00 50.00
2000 2001 2002 2003 year
%
E C W
Figure 5 The average target of electricity savings ratios in the three major areas of mainland China
During the 2000-2003 period, the average electricity consumption target savings ratios in three areas were stable. However, the average electricity consumption savings ratios of the west area stayed around 40% during the 2000-2003 period, showing no significant improvement.
Table 15 shows the Mann-Whitney test on target electricity savings ratios for three major areas in mainland China during 2000-2003. It presents that the target savings ratio of west area is significantly more than central area, which of the west area is significantly more than east area; and which of central area is not significantly more than east area. Summary of target electricity savings for three areas, east area and central area are more efficient and west area is most inefficient.
Table 15 Mann-Whitney test on target electricity savings ratios for three major areas
Note: ***represents significance at the 0.01 level; **represents significance at the 0.05 level.
4.1.2 Benchmark for Types of Energy Savings Ratios
From Tables 10, 12, 14 and 16 the five regions in mainland China are found to always have zero target savings ratios of three major types of energy, implying that their three major types of energy are efficient during the research period. One of these regions is located in the central area: Hunan (18), others are located in the east area:
Shanghai (09), Fujian (13), Shandong (15), and Guangdong (19). It shows that the above five regions are the benchmark for the industrial three major types of energy savings ratios.
4.1.3 General Comments on Three Types of Energy Savings
From Table 17, the four-year average target savings ratios of coal consumption for the east, central, and west areas are respectively 18.58%, 44.00%, and 59.80%.
The four-year average target savings ratios of gasoline consumption for the east, central, and west areas are respectively 13.43%, 22.70%, and 45.04%. The four-year average target savings ratios of electricity consumption for the east, central, and west areas are respectively 8.55%, 16.42%, and 43.70%.
Our empirical findings of this part show that the east area has most of the efficient regions with respect to the three major types of energy. The east area has the lowest average target savings ratios for the three major types of energy. Therefore, the west area consumed the highest grade of energy, but they still cannot provide better living standard. This means that the least-developed west area is using environmental goods inefficiently.
Comparing to those cases of gasoline and electricity, the average target savings ratios for coal consumption are relatively much higher in all three areas. This shows that coal reduction is mainland China’s most urgent task.
Table 16 Average overall technical efficiency for regions in mainland China during 2000-2003
ID Region Area 2000 2001 2002 2003
01 Beijing E 0.82 0.89 0.93 0.97
02 Tianjin E 0.87 0.90 0.93 0.97
03 Hebei E 1.00 1.00 0.99 0.99
04 Shanxi C 0.54 0.55 0.58 0.63
05 Inner Mongolia C 0.63 0.65 0.67 0.71
06 Liaoning E 0.78 0.70 0.72 0.78
07 Jilin C 0.72 0.76 0.79 0.83
08 Heilongjiang C 0.87 0.89 0.94 1.00 09 Shanghai E 1.00 1.00 1.00 1.00
10 Jiangsu E 1.00 0.97 0.96 0.96
11 Zhejiang E 0.87 0.88 0.93 0.95
12 Anhui C 1.00 1.00 1.00 0.97
13 Fujian E 1.00 1.00 1.00 1.00
14 Jiangxi C 0.98 1.00 1.00 1.00 15 Shandong E 1.00 1.00 1.00 1.00
16 Hennan C 0.96 0.96 0.93 0.97
17 Hubei C 0.87 0.92 0.92 0.95
18 Hunan C 1.00 1.00 1.00 1.00
19 Guangdong E 1.00 1.00 1.00 1.00
20 Guangxi E 0.91 0.92 0.88 0.88
21 Sichuan W 0.75 0.74 0.77 0.81
22 Guizhou W 0.65 0.64 0.65 0.62
23 Yunnan W 0.76 0.74 0.74 0.75
24 Shaanxi W 0.54 0.57 0.58 0.63
25 Gansu W 0.51 0.53 0.53 0.53
26 Qinghai W 0.45 0.47 0.52 0.51
27 Xinjiang W 0.79 0.79 0.81 0.89
Table 17 Average annual target savings ratios for regions in mainland China during
Note: Abatement ratios are in percentage terms.
4.2 Waste of Mainland China
4.2.1 Regional Abatements Ratios for Three Industrial Wastes
This part shows the regional target abatements ratios of three industrial wastes during 2000-2003, and the average regional target abatements ratios of three industrial wastes during 2000-2003. Figures show the trends of regional target abatements ratios in the three major areas of mainland China.
4.2.1.1 Abatements Ratios for Solid Wastes Produced
As Table 18, the east area has three regions with solid waste produced abatements ratios always higher than 30% throughout the research period: Hebei (03), Liaoning (06), and Guangxi (20). The central area has eight regions with solid waste produced target abatements ratios always higher than 30%: Shanxi (04), Inner Mongolia (05), Jilin (07), Heilongjiang (08), Anhui (12), Jiangxi(14), Hennan (16), and Hubei (17),
As Table 18, the east area has three regions with solid waste produced abatements ratios always higher than 30% throughout the research period: Hebei (03), Liaoning (06), and Guangxi (20). The central area has eight regions with solid waste produced target abatements ratios always higher than 30%: Shanxi (04), Inner Mongolia (05), Jilin (07), Heilongjiang (08), Anhui (12), Jiangxi(14), Hennan (16), and Hubei (17),