Review of existing empirical studies

The development of theoretical research is inseparable from the continuous verification of empirical research. Table 1.1 lists some important empirical documents. Through combining and summarizing these documents, the following conclusions can be drawn.

In terms of research objects, foreign countries focus on micro-scale research, and domestic focus on macro-level analysis

The research objects of most foreign literatures are mainly based on the production activities of micro-enterprises. In the selection of micro-enterprises, it is important to consider industrial production-oriented enterprises that are heavily invested or dependent on certain polluting raw materials in the production process, such as thermal power stations, which use fossil energy to burn and emit a lot of pollution. Gases, such as SO,, not only cause greater pollution to the atmosphere, but also affect human health. Therefore, in the existing literature, research on environmentally sensitive productivity studies using SO, emitted from power stations as undesired outputs is dominant

Table 1.1 Summary of evidence-based literature on studies about environmentally sensitive productivity

Authors

Evidence-based methods

Data

Variables

Main results

Model

Functions

Assessment

Input

Desirable Output

Undesirable

Output

Productivity

Shadow price

Gallop and Roberts (1985)

Cost

function

-

Minimum cost method

56 power plants in the US from 1973 to 1979

Labor/capital/ low-sulfur fuels/high-sulfur fuels

Electricity generated

SO,

-

SO,: 0.195 (USS/pounds, 1979 price)

Fare et al.

(1993)

DF(O)

Translog

Parameter

method

30 papermaking factories in the US in 1976

Paper pulp/ energy/ capital/ labor

Paper

BOD/TSS/PART/ so.

Efficiency: 0.9182

BOD: 1,043.4, TSS: 0, PART: 25,270, SO,: 3,696

(USS/ton, 1976 price)

Coggins and Swinton

(1996)

DF(O)

Translog

Parameter

method

14 heating and power plants in Wisconsin in 1990-1992

Sulfide/energy/ labor/ capital

Electricity generated

SO,

Efficiency: 0.946

SO,: 305(1990), 251.6(1991), 322.9(1992), Average 292.7

(USS/ton, 1992 price)

Boyd et al.

(1996)

DDF

-

DEA

Coal-fueled power plants in the US

Yaisawarng and Klein 1994 data

Fuel/labor/ capital (fixed input)/sulfur (undesirable output)

Net

electricity

SO,

Average

efficiency: 0.933

SO,: 1,703 (USS/ton, 1973 price)

Chung et al.

(1997)

DDF

DEA

30 Swedish papermaking factories in 1986-1990

Labor/wood fiber/energy/ capital

Pulp

BOD/COD/TSS

M index:0.997 (improved efficiency:0.977; technological progress: 1.02)

ML index: 1.039 (improved efficiency: 0.955; technological progress: 1.088)

Kolstad and Turnovsky (1998)

-

Quadric form

51 coalgenerated power plants in eastern America in 1970-1979

Sulfur/ash/ capital/ thermal energy

Electricity generated

SO,

-

SO,: 0.071;

Ash: 0.121 (USS/pounds, price in 1976)

Swinton

(1998)

DF(O)

Translog

Parameter method

Coal-fueled power plants in 1990-1998 Florida

Energy/labor/ capital/ sulfur

Electricity generated

SO,

Efficiency: 0.978

SO,: 157.10 (USS/ton, 1996 price)

Murty and Kumar (2003)

DF(O)

Translog

Parameter method

SFA

Industries with water pollution issues in India (samples from 60 companies)

Capital/labor/ energy/ materials

Turnover

BOD/COD/TSS

Efficiency: 0.899

BOD: 0.246, COD: 0.0775 (million Rupee/ ton, 1994/95 price)

Hailu and

Veeman

(2000)

DF(I)

Translog

Parameter method

Aggregated data of Canadian papermaking sector for 36 years between 1959 and 1994

Energy/wood residue/ wood pulp/ other raw materials/ production labor/ managerial labor/ capital

Wood pulp/ newsprint/ paper board/ other types of paper

BOD/TSS

TE: 0.996, M

Index: 0.878, ML

Index: 1.044

BOD: 123,

TSS: 286 (USS/million ton, 1986 price)

(continued)

Authors

Evidence-based methods

Data

Variables

Main results

Model

Functions

Assessment

Input

Desirable Output

Undesirable Output

Productivity

Shadow price

Reig-Martinez et al. (2001)

DF(O)

Translog

Parameter method

18 pottery factories in Spain

Raw materials/ capital/ labor

Ceramic pavement

Cement/waste oil

Average

efficiency: 0.927

Cement: 336.6 (euro/ton), waste oil: 125.5 (euro/kg)

Lee et al.

(2002)

DDF

-

DEA

43 Korean power plants of 1990-1995

Installed capacity/ fuels/labor

Power generation

SO,/NO,/TSP

-

SO,: 3,107,

NO,: 17,393, TSP: 51,093 (USS/ton)

Salnykov and Zelenyuk (2005)

DDF

Translog

Parameter method

50 countries

Labor/arable land/energy/ capital/

GNP

COJSOJNO,

Efficiency: 0.8433

CO,: 331.89,

SO,: 59,997.95

NO,: 154,583.63

(USS/ton)

Atkinson and Dorfman (2005)

DF(I)

Translog

Parameter method

43 US private for-profit power plants in 1980, 1985, 1990, 1995

Energy/labor/ capital

Power generation

SO,

Classical

efficiency: 0.564277

LIBS

efficiency: 0.553187

  • 1980 SO,: 395.3:
  • 1985

SO,: 1,871.7;

1990

SO,: 556.8:

1995 SO,: 486.7 (USS/ton)

Lee(2005)

DF(I)

Translog

Parameter method

51 US thermal power generating units in 1977-1986

Capital/heat/ sulfide/ash

Power generation

Sulfur/ash

TE: 0.945

SO,: 167.4,

Àsh: 127.7 (USS/pounds, 1976 price)

Fare et al.

(2005)

DDF

Quadric form

Deterministic

parameters

209 US thermal power plants in 1993/1997

Labor/ installed capacity/ fuel

Power generation

SO,

  • 1993:0.814,
  • 1997:0.785
  • 1993 SO,: 1,117;
  • 1997 SO,: 1,974 (USS/ton)

SFA

1993: 0.798, 1997:0.804

  • 1993 SO,: 76,
  • 1997 SO,: 142 (USS/ ton, 1982-1984 price)

Kumar

(2006)

DDF

DEA

41 countries in 1971-1992

Labor /capital/ energy

GDP

CO,

M index: 0.9998 (Improved efficiency: 1.0019; technological progress:0.9981)

ML index: 1.0002 (Improved efficiency: 0.9997; Technological progress: 1.0006)

Fare et al.

(2007)

DDF

-

DEA

92 thermal power plants in the US in 1995

Capital/labor/ fuel heat (coal, oil and gas)

Power generation

SO,

NO,

-

-

Ke et al.

(2008)

DF(O)

Translog

Parameters method

30 provinces in China from 1996 to 2002

Capital/labor

GDP

SO,

East: 0.831,

Middle: 0.706, West: 0.682

East: 0.516, Middle: 0.508, West: 0.529 (hundred million yuan/ ton, 1996 price)

Van Ha et al.

(2008)

DF(O)

Translog

Parameter/ measurement assessment

63 papermaking workshops in Vietnam in 2003

Capital/labor/ energy/ waste paper/other materials/ social capital

Paper

BOD/COD/TSS

Efficiency: 0.72

BOD: 575.2,

COD: 1,429.7,

TSS: 3,354.8

(USS/ton, 2003

price)

(continued)

Table 1.1 (Cont. )

Au Ihors

Evidence-based methods

Data

Variables

Main results

Model

Functions

Assessment

Input

Desirable Output

Undesirable Output

Productivity

Shadow price

Ghorbani and Motallebi (2009)

DF(O)

Translog

Parameters method

85 dairy farms in Iran in 2006

Farm area/ energy/ labor/feed

milk

CH4/CO/N,O

-

CH4: 0.61.

CO,: 0.058, N,6: 0.59 (price ratio against milk)

Hu et al.

(2008)

DDF

DEA

30 provinces in China from 1999 to 2005

Capital/labor

GDP

CO./COD/SO./ waste water/ solid waste

Highest in the east and lowest in the west (specific value depends on the type of undesirable output)

Tu(2008)

DDF

DEA

Industrial enterprises above a designated size in 30 provinces from 1998 to 2005

Capital/ energy/ labor

Industrial added value

SO,

East: relatively harmonious relationship between industry and environment

Middle and west: imbalances between environmental protection and industrial growth

Wang et al.

(2008)

DDF

-

DEA

1980-2004

APEC 17 countries and regions

Capital/labor

GDP

CO,

ML: 1.0056 (Technological progress:0.76%)

-

(continued)

Tu (2009)

DDF

DEA

Industrial enterprises above a designated size in 30 provinces from 1998 to 2005

Capital/ energy/ labor

Industrial added value

SO2

SO: 2.09

(RMB, hundred million yuan/ ten thousand tons, 1998 price unchanged)

Wu (2009)

DDF

DEA

1998-2007

China's 31 provinces industrial sector

Capital/labor

Industrial added value

COD/ SO,

National average ML: 1.085 (contribution of technological progress 95.29%)

-

Yue & Liu

(2009)

Inversed _ output

Reciprocal

method

DDF

DEA

36 industrial sectors in China between 2001 and 2006

Capital/labor

Industrial added value

SO,

Efficiency

value:inverse

algorithm: 0.55;

reciprocal

approach: 0.49;

directional distance

function: 0.68

Yang & Shao

(2009)

DDF

DEA

30 provincial industrial sectors from 1998 to 2007

Capital/labor

Industrial added value

SO,

East: 0.886,

Middle: 0.703, West: 0.686

-

Zhou & Gu (2009)

DDF

DEA

Industry data of large and mediumsized industrial enterprises in Shanghai from 1997 to 2004

Capital/labor/ energy

Total industry

SO,

2006 technological efficiency index: 0.6437 (heavy industry); 0.7396 (light industry)

Authors

Evidence-based methods

Data

Variables

Main results

Model

Functions

Assessment

Input

Desirable Output

Undesirable Output

Productivity

Shadow price

Chen et al.

(2010)

DDF

DEA

Industrial enterprises above a designated size in 11 provinces in the east of China from 2000 to 2007

Capital/labor

Output value

SO,

ML: 0.902(2007)

Wang et al.

(2010)

DDF

-

DEA

30 provinces in China from 1998 to 2007

Capital/labor/ energy

Industrial added value

COD/ SO,

National average:0.712

(VRS); 0.657 (CRS)

-

Dong et al.

(2010)

-

-

DEA

  • 29 provinces,
  • 19952006

Capital/labor/ sown area/ energy

Actual regional GDP

Reciprocal of environmental pollution index

ML: 1.008

-

Wu et al.

(2010)

DDF

-

DEA

East/Central/

Western region, 2000-2007

Capital/labor/ human capital

GDP

COD/ SO2

-

-

Note: price shown is the price of the year unless there is special explanation.

DF(O): Output distance function; DF(I): input distance function; DDF: directional distance function; DEA: Data Envelope Analysis method; SFA: stochastic frontier approach; M index: Malmquist productivity index; ML index: Malmquist-Luenberger productivity index; VRS: variable scale; CRS: invariant scale; BOD: biochemical oxygen demand; COD: chemical oxygen demand; TSS: total suspended solids; PART: particles; SO, sulfur oxides:

(Gallop & Roberts, 1985; Coggins & Swinton, 1996; Lee et al., 2002; Fare et al., 2005, 2007); in addition, some industrial producers whose emissions are easily metered, such as BOD/COD emissions from paper mills, water pollutants (Fare et al., 1993; Chung et al., 1997; Hailu & Veernan, 2000; Van Ha et al., 2008), waste oils discharged from ceramic plants (Reig-Martinez et al., 2001) are also used in the environment.

As countries continue to pay attention to greenhouse gas (GHG) emissions issues, some scholars have begun to shift their perspectives to macro-level research. They usually measure and compare the environmental sensitivity productivity of major GHG emissions such as CO2 and NO,, which are equivalent in economic development level (such as OECD, transition economies) or geographically similar countries (such as APEC, ECJ) (Salnykov & Zelenyuk, 2005; Kumar, 2006; Wang Bing et al., 2008).

Limited by the lack of data at the domestic enterprise level, especially the pollutant data is difficult to obtain. Most of the research on China is based on the macro-level, mainly to measure the environmental sensitivity productivity and the marginal abatement cost of pollutants in different provinces or industries. For example, Ke et al. (2008) used the output distance function and the super-logarithmic function form to measure the environmental sensitivity productivity of 30 provinces in mainland China from 1996 to 2002, and estimated the shadow of SO, pollutants. Hu Angang et al.

  • (2008) used the directional distance function earlier, using CO,, COD, SO,, total wastewater discharge and total solid waste discharge as indicators of undesired output, measuring 30 provinces in mainland China environmentally sensitive productivity during 1999-2005. At the industry level, there are mainly Tu Zhengge (2008, 2009), Wu Jun (2009), Yue Shujing and Liu Fuhua
  • (2009), Yang Jun and Shao Hanhua (2009), Zhou Jian and Gu Liuliu (2009), Chen Ru et al. (2010). Based on the SO, data of China's provincial industrial sector, the scholars used the directional distance function to measure the environmental sensitivity productivity of the industrial sectors in various provinces; at the regional level, Wang Bing et al. (2010), Dong Feng et al.
  • (2010), Wu Jun (2010) adopts provincial input-output data. In addition to capital and labor, the input includes energy consumption, human capital and other factors. The output end includes two Eleventh Five-Year Plan of COD and SO,. The pollutants required to be forced to reduce emissions use the directional distance function to calculate the environmentally sensitive productivity at the provincial level. In addition, in the study of Tu Zhenge (2008), Wang Bing et al. (2010) scholars, the factors affecting environmentally sensitive productivity were further analyzed and the environmental Kuznets curve and pollution paradise hypothesis have been empirically tested. These studies have important implications for understanding the differences in environmentally sensitive productivity between industries and regions, but they are limited by data factors, and their microscopic mechanisms are often not revealed.
 
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