Skip to main content

Advertisement

SpringerLink
Log in

Measuring the bias of technical change of industrial energy and environment productivity in China: a global DEA-Malmquist productivity approach

  • Research Article
  • Published:
Environmental Science and Pollution Research Aims and scope Submit manuscript

Abstract

Thanks to the booming industry, China has made a huge economic achievement during the past several decades. However, it is suffering severe environmental and sustainable problems now. To find a sustainable development path, it is necessary to assess Chinese industrial energy and environment productivity and explore the contributing reasons. It is known that the technical change is the one power that drives the growth of the industrial productivity. Nevertheless, the technical change bias of Chinese industrial energy and environment productivity has rarely been analyzed, such that the secrets of Chinese industrial energy and environment productivity cannot be further uncovered. Thus, in this paper, we first propose a global DEA-Malmquist productivity index to evaluate the industrial energy and environment productivity of China and then figure out the Chinese industrial technical change biases by relaxing the Hicks’ neutral assumption and decomposing the industrial technical change. We find out that both the global DEA-Malmquist productivity and the technical change are increased. Furthermore, the technical change drives the improvement of the global Malmquist productivity, but the technical progress is mainly driven by labor, energy consumption and CO2 emission biases. A multinomial logistic model is employed to find out the reasons for these biases. It finds that (1) the economic foundation has a significant positive impact on labor bias, while the infrastructures have negative impacts on labor bias. (2) CO2 emission bias is influence by energy prices positively. (3) The energy prices and the energy consumption structure have a negative influence on labor and energy bias, but the cost of curbing air pollutants and the size of the firm influence labor and energy bias positively. (4) The infrastructures and energy prices affect energy and CO2 emission bias positively, and the economic foundation and the size of the firm have negative impacts on energy and CO2 emission bias.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4.

Similar content being viewed by others

Data availability

The datasets generated and analyzed during the current study are not publicly available, but are available from the corresponding author on reasonable request.

Abbreviations

IPCC:

International Panel on Climate Change

m :

The number of inputs

GHS:

Greenhouse gases

p :

The number of desirable outputs

TFP:

Total factor productivity

q :

The number of undesirable outputs

DEA:

Data envelopment analysis

F :

The number of time periods

DDF:

Directional distance function

T :

Technology set

SBM:

Slacks-based measure

G :

The total time periods

MRS:

Marginal rate of substitution

s :

Index of time periods numbers

MRT:

Marginal rate of transformation

M G :

The global Malmquist productivity index

DMU:

Decision making units

D G :

The distance of DMU to the global frontier

EC:

Efficiency change

\( {d}_b^{-} \) :

The slacks of undesirable outputs

MG:

Malmquist productivity

\( {\zeta}_x^{-} \) :

The transformed slacks of inputs

BPC:

Best practice gap change

\( {\zeta}_y^{+} \) :

The transformed of desirable outputs

OBPC:

Output biased technical change

\( {\zeta}_b^{-} \) :

The transformed of undesirable outputs

IBPC:

Input biased technical change

D t :

The distance of DMU to the frontier in tth time period

MBPC:

Magnitude of technical change

D t + 1 :

The distance of DMU to the frontier in t + 1th time period

SCE:

Standard coal equivalent

p 1(x):

The output possibility set in period 1

E:

Total energy consumption

p 2(x):

The output possibility set in period 2 parallels p1(x)

NCV:

Net calorific value

p 21(x),p 22(x):

The output possibility sets in period 2 parallel p1(x)

CEF:

Carbon emission factor

p HG(x):

The global production possibility set parallels p1(x).

COF:

Carbon oxidation factor

p BG(x):

The global production possibility set parallels p1(x)

MNLM:

Multinomial logit model

L 1(y):

The isoquant in period 1

GDP:

Gross domestic product

L 2(x):

The isoquant in period 2 parallels L1(y)

L 21(y),L 22(y)):

The isoquants in period 2 do not parallel L1(y)

A:

Labor bias

L HG(y):

The global isoquant parallels L1(y)

B:

Energy bias

L BG(y):

The global production possibility set does not parallel L1(x)

E:

Labor and CO2 emission bias

C:

CO2 emission bias

F:

Energy and CO2 emission bias

D:

Labor and energy bias

R:

Labor and energy and CO2 emission bias

N:

Industrial Output values bias

M:

Capital bias

x ij :

ith input of jth DMU

\( {y}_{ij}^t \) :

rth desirable output of jth DMU in tth period of time

y rj :

rth desirable output of jth DMU

\( {b}_{ij}^t \) :

kth undesirable output of jth DMU in tth period of time

b kj :

kth undesirable output of jth DMU

λ j :

Intensity variables of DMUj

DMUj :

jth DMU

δ :

Any non-negative number

E o :

Efficiency scores of the target DMU

\( {x}_{ij}^t \) :

ith input of jth DMU in tth period of time

References

  • Alem Y, Beyene AD, Köhlin G, Mekonnen A (2016) Modeling household cooking fuel choice: A panel multinomial logit approach. Energy Econ 59:129–137

    Article  Google Scholar 

  • Althin R (2001) Measurement of productivity changes: two Malmquist index approaches. J Prod Anal 16(2):107–128

    Article  Google Scholar 

  • An Q, Wu Q, Li J, Xiong B, Chen X (2019) Environmental efficiency evaluation for Xiangjiang River basin cities based on an improved SBM model and Global Malmquist index. Energy Econ 81:95–103

    Article  Google Scholar 

  • Barros CP, Weber WL (2009) Productivity growth and biased technological change in UK airports. Transport Res E-Log 45(4):642–653

    Article  Google Scholar 

  • Barros CP, Managi S, Matousek R (2009) Productivity growth and biased technological change: Credit banks in Japan. J Int Financ Mark Inst Money 19(5):924–936

    Article  Google Scholar 

  • Barros CP, Managi S, Yoshida Y (2010) Productivity growth and biased technological change in japanese airports. Transp Policy 17(4):259–265

    Article  Google Scholar 

  • Barros CP, Guironnet JP, Peypoch N (2011) Productivity growth and biased technical change in French higher education. Econ Model 28(1-2):641–646

    Article  Google Scholar 

  • Berg SA, Førsund FR, Jansen ES (1992) Malmquist indices of productivity growth during the deregulation of norwegian banking, 1980–89. Scand J Econ  S211–S228

  • Briec W, Peypoch N (2007) Biased technical change and parallel neutrality. J Econ 92(3):281–292

    Article  Google Scholar 

  • Briec W, Peypoch N, Ratsimbanierana H (2011) Productivity growth and biased technological change in hydroelectric dams. Energy Econ 33(5):853–858

    Article  Google Scholar 

  • Chambers RG, Chung Y, Färe R (1996) Benefit and distance functions. J Econ Theory 70(2):407–419

    Article  Google Scholar 

  • Charnes A, Cooper WW (1962) Programming with linear fractional functionals. Nav Res Logist 9(3–4):181–186

    Article  Google Scholar 

  • Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2(6):429–444

    Article  Google Scholar 

  • Chen Z, Fan WD (2019) A multinomial logit model of pedestrian-vehicle crash severity in North Carolina. Int J Transp Sci Technol 8(1):43–52

    Article  Google Scholar 

  • Chen PC, Yu MM (2014) Total factor productivity growth and directions of technical change bias: evidence from 99 OECD and non-OECD countries. Ann Oper Res 214(1):143–165

    Article  Google Scholar 

  • Chung YH, Färe R, Grosskopf S (1997) Productivity and undesirable outputs: a directional distance function approach. J Environ Manag 51(3):229–240

    Article  Google Scholar 

  • Ding L, Yang Y, Wang W, Calin AC (2019) Regional carbon emission efficiency and its dynamic evolution in China: A novel cross efficiency-malmquist productivity index. J Clean Prod 241:118260

    Article  Google Scholar 

  • Du J, Chen Y, Huang Y (2018) A modified Malmquist-luenberger productivity index: Assessing environmental productivity performance in China. Eur J Oper Res 269(1):171–187

    Article  Google Scholar 

  • Emrouznejad A, Yang GL (2016a) CO2 emissions reduction of Chinese light manufacturing industries: a novel RAM-based global Malmquist–Luenberger productivity index. Energy Policy 96:397–410

    Article  CAS  Google Scholar 

  • Emrouznejad A, Yang GL (2016b) A framework for measuring global Malmquist–Luenberger productivity index with CO2 emissions on Chinese manufacturing industries. Energy 115:840–856

    Article  CAS  Google Scholar 

  • Fan M, Shao S, Yang L (2015) Combining global Malmquist–Luenberger index and generalized method of moments to investigate industrial total factor CO2 emission performance: A case of Shanghai (China). Energy Policy 79:189–201

    Article  CAS  Google Scholar 

  • Färe R, Grosskopf S (1997) Intertemporal production frontiers: with dynamic DEA. J Oper Res Soc 48(6):656–656

    Article  Google Scholar 

  • Färe R, Grosskopf S, Roos P (1995) Productivity and quality changes in Swedish pharmacies. Int J Prod Econ 39(1-2):137–144

    Article  Google Scholar 

  • Färe R, Grifell-Tatjé E, Grosskopf S, Lovell CAK (1997) Biased Technical Change and the Malmquist Productivity Index. Scand J Econ 99:119–127

    Article  Google Scholar 

  • Gao Y, Rasouli S, Timmermans H, Wang Y (2014) Reasons for not buying a car: A probit-selection multinomial logit choice model. Procedia Environ Sci 22:414–422

    Article  Google Scholar 

  • Hampf B, Krüger JJ (2017) Estimating the bias in technical change: A nonparametric approach. Econ Lett 157:88–91

    Article  Google Scholar 

  • Jun Z, Guiying W, Jipeng Z (2004) The Estimation of China's provincial capital stock: 1952—2000. Econ Res J 10(1):35–44

    Google Scholar 

  • Kao C (2010) Malmquist productivity index based on common-weights DEA: The case of Taiwan forests after reorganization. Omega 38(6):484–491

    Article  Google Scholar 

  • Kao C, Hwang SN (2014) Multi-period efficiency and Malmquist productivity index in two-stage production systems. Eur J Oper Res 232(3):512–521

    Article  Google Scholar 

  • Kumar S (2006) Environmentally sensitive productivity growth: a global analysis using Malmquist–Luenberger index. Ecol Econ 56(2):280–293

    Article  Google Scholar 

  • Lee J, Yasmin S, Eluru N, Abdel-Aty M, Cai Q (2018) Analysis of crash proportion by vehicle type at traffic analysis zone level: A mixed fractional split multinomial logit modeling approach with spatial effects. Accid Anal Prev 111:12–22

    Article  Google Scholar 

  • Liu FHF, Wang PH (2008) DEA Malmquist productivity measure: Taiwanese semiconductor companies. Int J Prod Econ 112(1):367–379

    Article  Google Scholar 

  • Liu X, Zhou D, Zhou P, Wang Q (2017) Dynamic carbon emission performance of Chinese airlines: a global Malmquist index analysis. J Air Transp Manag 65:99–109

    Article  Google Scholar 

  • Liu H, Yang R, Wu D, Zhou Z (2021) Green productivity growth and competition analysis of road transportation at the provincial level employing Global Malmquist-Luenberger Index approach. J Clean Prod 279:123677

    Article  Google Scholar 

  • Long R, Ouyang H, Guo H (2020) Super-slack-based measuring data envelopment analysis on the spatial-temporal patterns of logistics ecological efficiency using global Malmquist Index model. Environ Technol Innov 18:100770

  • Ma JJ, Du G, Xie BC (2019) CO2 emission changes of China's power generation system: Input-output subsystem analysis. Energy Policy 124:1–12

    Article  Google Scholar 

  • Malmquist S (1953) Index numbers and indifference surfaces. Trab Estad 4(2):209–242

    Google Scholar 

  • Margaritis D, Scrimgeour F, Cameron M, Tressler J (2005) Productivity and economic growth in Australia. New Zealand and Ireland Agenda, 12(4), 291–308

  • Mavi NK, Mavi RK (2019) Energy and environmental efficiency of OECD countries in the context of the circular economy: Common weight analysis for malmquist productivity index. J Environ Manag 247:651–661

    Article  Google Scholar 

  • Mavi RK, Fathi A, Saen RF, Mavi NK (2019) Eco-innovation in transportation industry: A double frontier common weights analysis with ideal point method for Malmquist productivity index. Resour Conserv Recycl 147:39–48

    Article  Google Scholar 

  • McFadden D (1974) Conditional logit analysis of qualitative choice behavior. In: Zarembka P (ed) Frontiers in econometrica. Academic press, New York

    Google Scholar 

  • Mizobuchi H (2015) Multiple directions for measuring biased technical change. School of Economics, University of Queensland

  • Oh DH, Lee JD (2010) A metafrontier approach for measuring Malmquist productivity index. Empir Econ 38(1):47–64

    Article  Google Scholar 

  • Pastor JT, Lovell CAK (2005) A global malmquist productivity index. Econ Lett 88(2):266–271

    Article  Google Scholar 

  • Pastor JT, Asmild M, Lovell CAK (2011) The biennial Malmquist productivity change index[J]. Socio Econ Plan Sci 45(1):10–15

    Article  Google Scholar 

  • Simon E, Leandro B, Kyoko M, Todd N, Kiyoto T (2006) IPCC Guidelines for National Greenhouse Gas Inventories. Institute for Global Environmental Strategies (IGES). Kanagawa , Japan. 4.48-4.62. Available at https://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/2_Volume2/V2_4_Ch4_Fugitive_Emissions.pdf

  • Sueyoshi T, Goto M (2013) DEA environmental assessment in a time horizon: Malmquist index on fuel mix, electricity and CO2 of industrial nations. Energy Econ 40:370–382

    Article  Google Scholar 

  • The United Nations, UN International Panel on Climate Change report 2018. Available at https://news.un.org/zh/story/2018/10/1019992

  • Tohidi G, Razavyan S (2013) A circular global profit Malmquist productivity index in data Envelopment analysis. Appl Math Model 37(1-2):216–227

    Article  Google Scholar 

  • Tone K (2001) A slacks-based measure of efficiency in data envelopment analysis. Eur J Oper Res 130(3):498–509

    Article  Google Scholar 

  • Tone K (2004) Dealing with undesirable outputs in DEA: A slacks-based measure (SBM) approach. Presentation At NAPW III, Toronto, 44–45

  • Vajari MA, Aghabayk K, Sadeghian M, Shiwakoti N (2020) A multinomial logit model of motorcycle crash severity at Australian intersections. J Saf Res 73:17–24

  • Wang YM, Lan YX (2011) Measuring Malmquist productivity index: A new approach based on double frontiers data envelopment analysis. Math Comput Model 54(11-12):2760–2771

    Article  Google Scholar 

  • Wang X, Wang Y (2020) Regional unified environmental efficiency of China: a non-separable hybrid measure under natural and managerial disposability. Environ Sci Pollut Res 27:27609–27625

  • Wang XL, Fan G, Yu JW (2016) Provincial marketization index in China. Social Sciences Academic Press

  • Wang KL, Pang SQ, Ding LL, Miao Z (2020) Combining the biennial Malmquist–Luenberger index and panel quantile regression to analyze the green total factor productivity of the industrial sector in China. Sci Total Environ 739:140280

    Article  CAS  Google Scholar 

  • Yu MM, Hsu CC (2012) Service Productivity and Biased Technological Change of Domestic Airports in Taiwan. Int J Sustain Transp 6(1):1–25

    Article  Google Scholar 

  • Zhao L, Zha Y, Liang N, Liang L (2016) Data envelopment analysis for unified efficiency evaluation: An assessment of regional industries in China. J Clean Prod 113:695–704

    Article  Google Scholar 

  • Zhou WQ (2013) China's industrial productivity growth and its influencing factors constrained by carbon emissions. Huazhong University of Science and Technology

Download references

Funding

This study was supported by the National Nature Science Foundation of China under the Grant Nos. 61773123 and 71701050; it is also partially supported by the Major research project of Fujian Social Science Research Base under the Grant No. FJ2020MJDZ016.

Author information

Authors and Affiliations

  1. Decision Sciences Institute, Fuzhou University, 2 Wulongjiang North Avenue, Fuzhou, People’s Republic of China

    Xu Wang, Yingming Wang & Yixin Lan

  2. Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University, 2 Wulongjiang North Avenue, Fuzhou, People’s Republic of China

    Yingming Wang

  3. School of Economics & Management, Fuzhou University, 2 Wulongjiang North Avenue, Fuzhou, People’s Republic of China

    Yingming Wang

Authors
  1. Xu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yingming Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yixin Lan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

XW analyzed and interpreted the findings regarding the bias of technical change of industrial energy and environment productivity in China, and was a major contributor in writing the manuscript. YW proposed the study idea and constructed the corresponding formulations, and was the main designer of this study. YL collected and maintained the data for analysis, and reviewed the paper. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yingming Wang.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Responsible Editor: Eyup Dogan

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, X., Wang, Y. & Lan, Y. Measuring the bias of technical change of industrial energy and environment productivity in China: a global DEA-Malmquist productivity approach. Environ Sci Pollut Res 28, 41896–41911 (2021). https://doi.org/10.1007/s11356-021-13128-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-021-13128-w

Keywords

Search

Navigation