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Front. Eng    2022, Vol. 9 Issue (3) : 425-438    https://doi.org/10.1007/s42524-022-0205-5
RESEARCH ARTICLE
Sustainability performance analysis of environment innovation systems using a two-stage network DEA model with shared resources
Jiangjiang YANG1, Jie WU1, Xingchen LI2(), Qingyuan ZHU3()
1. School of Management, University of Science and Technology of China, Hefei 230026, China
2. School of Accounting, Nanjing Audit University, Nanjing 211815, China
3. College of Economics and Management, Research Center for Soft Energy Science, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
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Abstract

The term environmental innovation system refers to an innovation network composed of enterprises, universities, and research institutions involved in the development and diffusion of environmental technology, with the participation of a government. An environmental innovation system not only exerts important impact on the achievement of carbon neutrality but also affects social and economic activities. Investigations on environmental innovation system performance constantly assume a single-stage independent system while ignoring its internal structure. However, such systems are composed of environmental innovation research and development (R&D) and environmental innovation conversion subsystems. A two-stage data envelopment analysis (DEA) model is developed in this study to analyze the efficiency of Chinese regional environmental innovation system by opening the “black box” and considering shared resources. Empirical results indicated that China presents high overall environmental innovation efficiency although some regions need to improve. Regions with low efficiencies in both environmental innovation R&D (EIR) and environmental innovation conversion (EIC) subsystems should expand their investment in and strengthen the management of environmental innovation resources. Regions with low EIR efficiency should improve the absorption and transformation of environmental innovation achievements. Regions with low EIC efficiency should increase investment in the commercialization of environmental innovation achievements and encourage green economy industries, such as new energy, art, tourism, and environmental protection.
Keywords data envelopment analysis      environmental efficiency      environmental innovation system      shared resources      two-stage structure     
Corresponding Author(s): Xingchen LI,Qingyuan ZHU   
About author:

Tongcan Cui and Yizhe Hou contributed equally to this work.
Just Accepted Date: 24 May 2022   Online First Date: 03 August 2022    Issue Date: 05 September 2022
 Cite this article:   
Jiangjiang YANG,Jie WU,Xingchen LI, et al. Sustainability performance analysis of environment innovation systems using a two-stage network DEA model with shared resources[J]. Front. Eng, 2022, 9(3): 425-438.
 URL:  
https://academic.hep.com.cn/fem/EN/10.1007/s42524-022-0205-5
https://academic.hep.com.cn/fem/EN/Y2022/V9/I3/425
Fig.1  The two-stage environmental innovation system with shared inputs.
Variables 2015 2016 2017 2018 2019
Expenditure on R&D Min 115842.70 139976.70 179108.60 172951.10 205680.10
Max 18012271.00 20351439.90 23436283.20 27046969.00 30984890.00
Mean 4722253.49 5224843.35 5867754.90 6558074.18 7379747.62
SD 5182068.27 5778686.80 6492727.04 7234770.81 8117404.69
New products Min 24.00 37.00 36.00 49.00 72.00
Max 15127.00 22541.00 32392.00 38526.00 46263.00
Mean 2571.93 3104.27 3795.80 4387.10 5223.87
SD 3717.68 4865.39 6462.94 7550.92 8997.01
Green patent applications Min 124.00 145.00 155.00 256.00 342.00
Max 13554.00 15575.00 18693.00 28621.00 26176.00
Mean 3282.57 3890.50 4175.03 5923.47 5534.67
SD 3720.31 4292.47 4770.06 6901.04 6484.31
Electricity Min 272.36 287.31 304.95 327.00 354.89
Max 5310.69 5610.13 5958.97 6323.00 6695.85
Mean 1896.42 1989.93 2118.90 2279.30 2413.60
SD 1346.39 1433.11 1502.58 1604.27 1676.25
Labor Min 321.41 324.28 326.97 329.26 330.20
Max 6723.30 6726.39 6766.86 7132.99 7150.25
Mean 2777.57 2766.12 2766.24 2770.74 2750.14
SD 1809.62 1804.02 1801.15 1830.15 1812.12
R&D full-time equivalent Min 4007.70 4165.60 5655.80 4301.10 5476.00
Max 520302.50 543437.70 565287.30 762733.30 803207.80
Mean 125257.25 129231.05 134411.64 145995.87 159967.29
SD 135187.98 139860.03 148161.03 174246.07 189819.45
Gross domestic product (GDP) Min 2417.05 2572.49 2624.83 2865.23 2965.95
Max 72812.55 80854.91 89705.23 97277.77 107671.07
Mean 24058.05 25963.95 28194.31 30440.99 32787.84
SD 18046.47 19937.99 22025.35 23731.16 25774.58
Carbon emissions Min 3647.83 4518.03 4231.69 6326.27 3953.70
Max 105702.99 110811.29 110615.20 121494.22 125030.31
Mean 34186.58 34440.09 35424.97 37264.38 37904.16
SD 23181.53 23852.49 24527.65 26589.50 28425.38
Tab.1  Statistical descriptions of the dataset
Indicator Variable Units Definition
Input in EIR Expenditure on R&D 10000 yuan Intramural expenditure on R&D by provincial region
Output in EIR New products Unit Number of new product development projects
Intermediate output in EIR (Input in EIC) Green patent applications Piece Number of green patent applications
Shared input R&D full-time equivalent Person-year Total workload of full-time and part-time personnel
Input in EIC Electricity 100 million kWh Regional consumption of electricity
Labor 10000 persons Number of full-time employees of a specific region in China
Desirable outputs in EIC GDP 100 million yuan Gross domestic product
Undesirable outputs in EIC Carbon emissions 10 thousand tons Annual regional carbon emissions
Tab.2  Variables and definitions
Fossil fuels Coal Petrol Kerosene Diesel Fuel oil Natural gas
CCF 27.28 18.90 19.60 20.17 21.09 15.32
HE 192.14 448.00 447.50 433.30 401.90 0.38
COF (%) 92.3 98.0 98.6 98.2 98.5 99.0
Tab.3  Carbon emission factors of major fossil fuel types in China
Region Ej0? e1j0? e2j0? αj0 1?αj0
Beijing 0.9690 0.9095 1.0000 0.25 0.75
Tianjin 0.8368 0.8433 0.8334 0.57 0.43
Hebei 0.7856 0.7174 0.8015 0.53 0.47
Shanxi 0.8216 0.6152 0.8595 0.53 0.47
Inner Mongolia 1.0000 1.0000 1.0000 0.56 0.44
Liaoning 0.8453 0.6524 0.9011 0.53 0.47
Jilin 0.8521 0.6202 0.9003 0.57 0.43
Heilongjiang 0.8237 0.6000 0.8667 0.63 0.37
Shanghai 0.9935 0.9751 1.0000 0.53 0.47
Jiangsu 0.8044 0.6890 0.8858 0.51 0.49
Zhejiang 0.7356 0.6906 0.7787 0.35 0.65
Anhui 0.8087 0.6012 0.8799 0.34 0.66
Fujian 1.0000 1.0000 1.0000 0.51 0.49
Jiangxi 0.9530 0.9413 0.9610 0.38 0.62
Shandong 0.8457 0.6814 0.9082 0.48 0.52
Henan 0.8998 0.7714 0.9459 0.55 0.45
Hubei 0.9771 0.8602 1.0000 0.51 0.49
Hunan 0.9941 0.9672 1.0000 0.36 0.64
Guangdong 1.0000 1.0000 1.0000 0.26 0.74
Guangxi 0.9847 0.9538 1.0000 0.60 0.40
Hainan 1.0000 1.0000 1.0000 0.55 0.45
Chongqing 1.0000 1.0000 1.0000 0.26 0.74
Sichuan 1.0000 1.0000 1.0000 0.26 0.74
Guizhou 0.8613 0.7430 0.8828 0.61 0.39
Yunnan 1.0000 1.0000 1.0000 0.51 0.49
Shaanxi 0.8662 0.6308 0.9337 0.57 0.43
Gansu 0.6787 0.7829 0.6139 0.62 0.38
Qinghai 0.9679 1.0000 0.9616 0.61 0.39
Ningxia 0.7319 0.8424 0.7085 0.57 0.43
Xinjiang 1.0000 1.0000 1.0000 0.57 0.43
Average 0.9012 0.8363 0.9208 0.49 0.51
Tab.4  Efficiencies of environmental innovation systems of 30 Chinese provincial-level regions in 2019
Fig.2  Overall efficiency of 30 Chinese regions from 2015 to 2019.
Fig.3  EIR efficiency of 30 Chinese regions from 2015 to 2019.
Fig.4  EIC efficiency of 30 Chinese regions from 2015 to 2019.
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