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Applying the Super-EBM model and spatial Durbin model to examining total-factor ecological efficiency from a multi-dimensional perspective: evidence from China

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Abstract

Ecological efficiency mainly emphasizes the importance of balancing the relationships between natural resources, energy, the ecological environment and economic growth, which has aroused widespread concern worldwide. China’s rapid economic development has inevitably been accompanied by serious resource exhaustion, environmental pollution and ecological deterioration in the past several decades, which has brought huge challenges to China’s sustainable development. Therefore, establishing the evaluation framework of total-factor ecological efficiency (TFEE) and identifying its driving force have a great significance for improving China’s sustainable development capabilities. First, an ecological efficiency evaluation framework is established based on the theory of total-factor analysis. Second, the super efficient hybrid distance model considers undesirable output and measures TFEE nationwide in 30 provinces and four regions during the period 2003–2017. Finally, the spatial effect of TFEE and its influencing factors are examined by using a spatial Durbin model. The empirical results show that (1) nationwide and regional TFEEs have different degrees of decline during the study period. There were significant differences among the 30 provinces and four regions. Beijing, Tianjin and Shanghai are efficient, while the other provinces have not been as effective. The TFEEs of the four regions are not effective with an ordering of eastern > northeast > central > western. (2) Moran’s I index shows that the TFEE nationwide has a positive spatial autocorrelation with strong spatial agglomeration. However, the spatial distribution pattern of TFEE in China was unstable and labile. The Moran scatter plot indicates that China’s provincial TFEE has not only spatial dependence characteristics but also differences in spatial correlation. (3) Most factors are bound up with TFEE to various degrees: technological progress (TP), industrial agglomeration (IG) and human capital (HC) play a positive role, while industrial structure (IS), the level of urbanization (CITY) and energy intensity (EI) play a negative role. Additionally, environmental regulation (GZ) shows a U-type relationship with TFEE. The level of economic development (GDP) and foreign direct investment (FDI) cannot have a significant impact on TFEE at this stage. (4) The spatial Durbin model results show that TFEE has a significant spatial spillover effect, and the improvement of the TFEE of a province will increase the TFEE of neighbouring provinces. The confirmed spatial spillover effects of technological progress (TP), industrial structure (IS), the level of urbanization (CITY), industrial agglomeration (IG) and human capital (HC) can significantly impact the TFEE of neighbouring provinces. Among them, technological progress (TP), the level of urbanization (CITY) and human capital (HC) can significantly improve the TFEE of neighbouring provinces, and the level of economic development (GDP) and foreign direct investment (FDI) can significantly inhibit the improvement of TFEE in neighbouring provinces.

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Data availability

The data used in this article can be found in the “China Statistical Yearbook”, “China Energy Statistical Yearbook” and “China Environment Yearbook”.

References

  • Alizadeh S, Zafari-koloukhi H, Rostami F et al (2020) The eco-efficiency assessment of wastewater treatment plants in the city of Mashhad using emergy and life cycle analyses. J Clean Prod 249:119327

    Google Scholar 

  • Andersen P, Petersen N (1993) A procedure for ranking efficient units in data envelopment analysis. Manag Sci 39:1261–1264

    Google Scholar 

  • Angeles GD, Emilio GD, Juan CP et al (2017) Assessing eco-efficiency and the determinants of horticultural family-farming in southeast Spain. J Environ Manag 204:594–604

    Google Scholar 

  • Anselin L, Florax R, Rey S (2004) Advanced in spatial econometrics: methodology tools and applications. Springer Science&Business Media, Berlin

  • Arabi B, Munisamy S, Emrouznejad A (2015) A new slacks-based measure of Malmquist-Luenberger index in the presence of undesirable outputs. Omega 51(51):29–37

    Google Scholar 

  • Avkiran K, Tone K, Tsutsui M (2008) Bridging radial and non-radial measures of efficiency in DEA. Ann Oper Res 164(1):127–138

    Google Scholar 

  • Bai Y, Deng X, Jiang S, Zhang Q, Wang Z (2018) Exploring the relationship between urbanization and urban eco-efficiency: evidence from prefecture-level cities in China. J Clean Prod 195:1487–1496

    Google Scholar 

  • Barak S, Dahooei JH (2018) A novel hybrid fuzzy DEA-Fuzzy MADM method for airlines safety evaluation. J Air Transp Manag 73:134–149

    Google Scholar 

  • Bianchi M, Valle ID, Tapia C (2020) Measuring eco-efficiency in European regions: evidence from a territorial perspective[J]. J Clean Prod 276:123246

    Google Scholar 

  • Caneghem J, Block C, Cramm P, Mortier R, Vandecasteele C (2010) Improving eco-efficiency in the steel industry: the ArcelorMittal Gent case. J Clean Prod 18(8):807–817

    Google Scholar 

  • Charmondusit K, Keartpakpraek K (2011) Eco-efficiency evaluation of the petroleum and petrochemical group in the map Ta Phut Industrial Estate, Thailand. J Clean Prod 19(2/3):241–252

    Google Scholar 

  • Charnes A, Cooper W, Rhodes E (1978) Measuring the Efficiency of Decision Making Units. Eur J Oper Res 2:429–444

    Google Scholar 

  • Dong F, Zhang Y, Zhang X (2020) Applying a data envelopment analysis game cross-efficiency model to examining regional ecological efficiency: Evidence from China. J Clean Prod 267:122031

    Google Scholar 

  • Elhorst J (2010) Applied spatial econometrics: raising the bar. Spat Econ Anal 5(01):9–28

    Google Scholar 

  • Elhorst J, Fréret S (2010) Evidence of political yardstick competition in France using a two-regime spatial Durbin model with fixed effects. J Reg Sci 49(05):931–951

    Google Scholar 

  • Ghimire SR, Johnston JM (2017) A modified eco-efficiency framework and methodology for advancing the state of practice of sustainability analysis as applied to green infrastructure. Integr Environ Assess 13(5):1–16

    Google Scholar 

  • Gössling S, Peeters P, Ceron JP et al (2015) The eco-efficiency of tourism. Ecol Econ 54(4):417–434

    Google Scholar 

  • Guan D, Liu Z, Geng Y (2012) The gigatonne gap in China’s carbon dioxide inventories. Nat Clim Chang 2(9):672–675

    CAS  Google Scholar 

  • Halkos G, Petrou KN (2019) Assessing 28 EU member states’ environmental efficiency in national waste generation with DEA. J Clean Prod 208:509–521

    CAS  Google Scholar 

  • Hu X, Liu C (2015) Slacks-based data envelopment analysis for eco-efficiency assessment in the Australian construction industry. Constr Manag Econ 35(11-12):1–14

    Google Scholar 

  • Huppes G, Davidson MD, Kuyper J et al (2007) Eco-efficient environmental policy in oil and gas production in The Netherland. Ecol Econ 61(1):43–51

    Google Scholar 

  • Ingaramo A, Heluane H, Colombo M, Cesca M (2009) Water and wastewater eco-efficiency indicators for the sugar cane industry. J Clean Prod 17(4):487–495

    Google Scholar 

  • Kharel GP, Charmondusit K (2008) Eco-efficiency evaluation of iron rod industry in Nepal. J Clean Prod 16(13):1379–1387

    Google Scholar 

  • Kounetasa KE, Polemis ML, Tzeremes NG (2020) Measurement of eco-efficiency and convergence: Evidence from a non-parametric frontier analysis. Eur J Oper Res 291(1):365–378

    Google Scholar 

  • LeSage J, Pace K (2009) Introduction to spatial econometrics. CRC Press, Boca Raton

  • Li D, Jin Z, Hui EC et al (2011) An emergy analysis-based methodology for eco-efficiency evaluation of building manufacturing. Ecol Indic 11(5):1419–1425

    Google Scholar 

  • Li G, Fang C, Wang S, Sun S (2016) The effect of economic growth, urbanization, and industrialization on fine particulate matter (PM2.5) concentrations in China. Environ Sci Technol 50(21):11452-11459

    Google Scholar 

  • Li C, Zhang S, Zhang W (2018) Spatial distribution characteristics and influencing factors of China's inter provincial industrial eco-efficiency. Sci Geogr Sin 38(12):1970–1978

    Google Scholar 

  • Litos L, Gray D, Johnston B, Morgan D, Evans S (2017) A maturity-based improvement method for eco-efficiency in manufacturing systems. Procedia Manuf 8:160–167

    Google Scholar 

  • Liu Z, Guan D, Wei W (2015) Reduced carbon emission estimates from fossil fuel combustion and cement production in China. Nature 524(7565):335–338

    CAS  Google Scholar 

  • Liu J, Zhang J, Fu Z (2017) Tourism eco-efficiency of Chinese coastal cities—analysis based on the DEA-Tobit model. Ocean Coast Manag 148:164–170

    Google Scholar 

  • Liu X, Guo P, Guo S (2018) Assessing the eco-efficiency of a circular economy system in China’s coal mining areas: emergy and data envelopment analysis. J Clean Prod 206:1101–1109

    Google Scholar 

  • Liu Q, Wang S, Li B et al (2020a) Dynamics, differences, influencing factors of eco-efficiency in China: a spatiotemporal perspective analysis. J Environ Manag 264:110442

    Google Scholar 

  • Liu Y, Zou L, Wang Y (2020b) Spatial-temporal characteristics and influencing factors of agricultural eco-efficiency in China in recent 40 years. Land Use Policy 97:104794

    Google Scholar 

  • Liu Z, Zhang H, Zhang YJ et al (2020c) How does industrial policy affect the eco-efficiency of industrial sector? Evidence from China. Appl Energy 272:115206

    Google Scholar 

  • Liu C, Gao M, Zhu G et al (2021) Data driven eco-efficiency evaluation and optimization in industrial production. Energy 224(11):120170

    Google Scholar 

  • Long X, Zhao X, Cheng F (2015) The comparison analysis of total factor productivity and eco-efficiency in China’s cement manufactures. Energy Policy 81:61–66

    Google Scholar 

  • Michelsen O, Fet AM, Dahlsrud A (2006) Eco-efficiency in extended supply chains: a case study of furniture production. J Environ Manag 79(3):290–297

    Google Scholar 

  • Mickwitz P, Melanen M, Rosenstr MU et al (2006) Regional eco-efficiency indicators a participatory approach. J Clean Prod 14(18):1603–1611

    Google Scholar 

  • Moutinho V, Madaleno M, Macedo P et al (2018) Efficiency in the European agricultural sector: environment and resources. Environ Sci Pollut Res 25(18):17927–17941

    Google Scholar 

  • Moutinho V, Madaleno M, Macedo P (2020) The effect of urban air pollutants in Germany: eco-efficiency analysis through fractional regression models applied after DEA and SFA efficiency predictions. Sustain Cities Soc 59:102204

    Google Scholar 

  • Organization for Economic Cooperation and Development (1998) Eco-efficiency Pairs, pp 7–11

  • Pace R, Lesage J (2009) A sampling approach to estimate the log determinant used in spatial likelihood problems. J Geogr Syst 11(3):209–225

    Google Scholar 

  • Ren Y, Fang C, Li G (2020) Spatiotemporal characteristics and influential factors of eco-efficiency in Chinese prefecture-level cities: a spatial panel econometric analysis. J Clean Prod 260:120787

    Google Scholar 

  • Robaina-Alves M, Moutinho V, Macedo P (2015) A new frontier approach to model the eco-efficiency in European countries. J Clean Prod 103:562–573

    Google Scholar 

  • Salmi O (2007) Eco-efficiency and industrial symbiosis-a counterfactual analysis of a mining community. J Clean Prod 15(17):1696–1705

    Google Scholar 

  • Schaltegger S, Sturm A (1990) Okologische rationalitat: ansatzpunktezur ausgestaltung vonokologieorientierten management instrumenten. Unternehmung 44(4):273–290

    Google Scholar 

  • Tan J, Wang R (2021) Research on evaluation and influencing factors of regional ecological efficiency from the perspective of carbon neutrality. J Environ Manag 294(2):113030

    Google Scholar 

  • Tang M, Li Z, Hu F et al (2020) How does urban land expansion promote urban eco-efficiency? The mediating effect of sophistication of industrial structure. J Clean Prod 272:122798

    Google Scholar 

  • Tone K (2001) Slacks-based measure of efficiency in data envelopment analysis. Eur J Oper Res 30:498–509

    Google Scholar 

  • Tone K, Tsutsui M (2010) An epsilon-based measure of efficiency in DEA—a third pole of technical efficiency. Eur J Oper Res 207(3):1554–1563

    Google Scholar 

  • Tu Z, Hu T, Shen R (2019) Evaluating public participation impact on environmental protection and ecological efficiency in China: evidence from PITI disclosure. China Econ Rev 55:11–123

    Google Scholar 

  • Van ME, Berentsen PM, Dolman MA et al (2011) Eco-efficiency in the production chain of Dutch semi-hard cheese. Livest Sci 139(1/2):91–99

    Google Scholar 

  • Wang X, Wu Q, Majeed S, Sun D (2018) Fujian’s industrial eco-efficiency: evaluation based on SBM and the empirical analysis of influencing factors. Sustainability 10(9):3333–3351

    Google Scholar 

  • World Business Council for Sustainable Development (2000) Eco-efficient leadership for improved economic and environmental performance. Geneva, pp 3–16

  • Wu Y, Ke Y, Xu C, Xiao X, Hu Y (2018) Eco-efficiency measurement of coal-fired power plants in China using super efficiency data envelopment analysis. Sustain Cities Soc 36:157–168

    Google Scholar 

  • Wursthorn S, Poganietz WR, Schebek L (2011) Economic-environmental monitoring indicators for European countries: a disaggregated sector-based approach for monitoring eco-efficiency. Ecol Econ 70(3):487–496

    Google Scholar 

  • Yago LT, Ian VR, Maria JA et al (2016) Benchmarking wastewater treatment plants under an eco-efficiency perspective. Sci Total Environ 566-567:468–479

    Google Scholar 

  • Yang L, Yang Y (2019) Evaluation of eco-efficiency in China from 1978 to 2016: based on a modified ecological footprint model. Sci Total Environ 662:581–590

    CAS  Google Scholar 

  • Zhang W, Li G (2020) Environmental decentralization, environmental protection investment, and green technology innovation. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-020-09849-z

  • Zhang J, Zhang Y (2003) Recalculating the capital of China and a review of Li and Tang’s article. Econ Res J 79:35–43

    Google Scholar 

  • Zhao Z, Bai Y, Wang G, Chen J, Yu J, Liu W (2018) Land eco-efficiency for new-type urbanization in the Beijing-Tianjin-Hebei Region. Technol Forecast Soc Chang 137:19–26

    Google Scholar 

  • Zhou C, Shi C, Wang S, Zhang G et al (2018) Estimation of eco-efficiency and its influencing factors in Guang dong province based on Super-SBM and panel regression models. Ecol Indic 86:67–80

    Google Scholar 

Download references

Funding

China Postdoctoral Science Foundation (No. 2021M691572), Key Program of National Social Science Foundation of China (No. 17AJL008) and National Natural Science Foundation of China Program (No.62073198).

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Authors and Affiliations

  1. College of Economics and Management, Nanjing University of Aeronautics and Astronautics, 211106, Nanjing, China

    Li Chengyu

  2. College of Economics and Management, Shandong University of Science and Technology, Qingdao, 266590, China

    Zhang Yongmei & Zhang Shiqiang

  3. University of Jinan, Jinan, 250022, China

    Zhang Shiqiang

  4. School of Public Economics and Administration, Shanghai University of Finance and Economics, Shanghai, 200433, China

    Wang Jianmei

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  1. Li Chengyu
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LC, ZY and ZS designed the whole study; LC and WJ conducted data collection, modeling and results analysis; LC wrote the paper.

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Correspondence to Zhang Yongmei.

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Chengyu, ., Yongmei, Z., Shiqiang, Z. et al. Applying the Super-EBM model and spatial Durbin model to examining total-factor ecological efficiency from a multi-dimensional perspective: evidence from China. Environ Sci Pollut Res 29, 2183–2202 (2022). https://doi.org/10.1007/s11356-021-15770-w

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