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.
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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
Althin R (2001) Measurement of productivity changes: two Malmquist index approaches. J Prod Anal 16(2):107–128
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
Barros CP, Weber WL (2009) Productivity growth and biased technological change in UK airports. Transport Res E-Log 45(4):642–653
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
Barros CP, Managi S, Yoshida Y (2010) Productivity growth and biased technological change in japanese airports. Transp Policy 17(4):259–265
Barros CP, Guironnet JP, Peypoch N (2011) Productivity growth and biased technical change in French higher education. Econ Model 28(1-2):641–646
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
Briec W, Peypoch N, Ratsimbanierana H (2011) Productivity growth and biased technological change in hydroelectric dams. Energy Econ 33(5):853–858
Chambers RG, Chung Y, Färe R (1996) Benefit and distance functions. J Econ Theory 70(2):407–419
Charnes A, Cooper WW (1962) Programming with linear fractional functionals. Nav Res Logist 9(3–4):181–186
Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2(6):429–444
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
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
Chung YH, Färe R, Grosskopf S (1997) Productivity and undesirable outputs: a directional distance function approach. J Environ Manag 51(3):229–240
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
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
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
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
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
Färe R, Grosskopf S (1997) Intertemporal production frontiers: with dynamic DEA. J Oper Res Soc 48(6):656–656
Färe R, Grosskopf S, Roos P (1995) Productivity and quality changes in Swedish pharmacies. Int J Prod Econ 39(1-2):137–144
Färe R, Grifell-Tatjé E, Grosskopf S, Lovell CAK (1997) Biased Technical Change and the Malmquist Productivity Index. Scand J Econ 99:119–127
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
Hampf B, Krüger JJ (2017) Estimating the bias in technical change: A nonparametric approach. Econ Lett 157:88–91
Jun Z, Guiying W, Jipeng Z (2004) The Estimation of China's provincial capital stock: 1952—2000. Econ Res J 10(1):35–44
Kao C (2010) Malmquist productivity index based on common-weights DEA: The case of Taiwan forests after reorganization. Omega 38(6):484–491
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
Kumar S (2006) Environmentally sensitive productivity growth: a global analysis using Malmquist–Luenberger index. Ecol Econ 56(2):280–293
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
Liu FHF, Wang PH (2008) DEA Malmquist productivity measure: Taiwanese semiconductor companies. Int J Prod Econ 112(1):367–379
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
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
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
Malmquist S (1953) Index numbers and indifference surfaces. Trab Estad 4(2):209–242
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
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
McFadden D (1974) Conditional logit analysis of qualitative choice behavior. In: Zarembka P (ed) Frontiers in econometrica. Academic press, New York
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
Pastor JT, Lovell CAK (2005) A global malmquist productivity index. Econ Lett 88(2):266–271
Pastor JT, Asmild M, Lovell CAK (2011) The biennial Malmquist productivity change index[J]. Socio Econ Plan Sci 45(1):10–15
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
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
Tone K (2001) A slacks-based measure of efficiency in data envelopment analysis. Eur J Oper Res 130(3):498–509
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
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
Yu MM, Hsu CC (2012) Service Productivity and Biased Technological Change of Domestic Airports in Taiwan. Int J Sustain Transp 6(1):1–25
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
Zhou WQ (2013) China's industrial productivity growth and its influencing factors constrained by carbon emissions. Huazhong University of Science and Technology
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.
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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.
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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
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DOI: https://doi.org/10.1007/s11356-021-13128-w