Skip to main content
SpringerLink
Log in

Does international trade contribute to eco-efficiency performance improvement? Evidence from the emerging and developing economies

  • Original Article
  • Published:
Energy Efficiency Aims and scope Submit manuscript

Abstract

We provide the first empirical study analyzing the effect of international trade on economy-wide eco-efficiency performances based on the emerging and developing economies. We extend the slack-based measure (SBM) model in Tone (Tone, 2004). Dealing with undesirable outputs in DEA: A slacks-based measure (SBM) approach. In: Presentation at NAPW III, Toront.) to the time-dependent conditional SBM model. The extended model proposed in this study considers the influences of exogenous variables and time, thereby circumventing the problem of biased estimates due to the assumption of a “separability” condition. Based on the comparison of conditional and unconditional eco-efficiency index (EEI) scores of 71 developing countries, we find that the improved conditional SBM model can more accurately measure the level of eco-efficiency. The nonparametric significance test and the location-scale test techniques are then employed to investigate the non-linear relationship between international trade and eco-efficiency performance. We find a U-shaped relationship between international trade and eco-efficiency performances of the emerging and developing economies, indicating that international trade had a negative effect on eco-efficiency performance when it was in the initial stage of development, while the effect becomes positive after international trade has reached a certain level. The policy implications for how to coordinate the relationship between trade openness, economic development, and ecological protection in developing countries are discussed in this article.

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

Similar content being viewed by others

Notes

  1. The original SBM model proposed by Tone (2001) does not account for undesirable outputs. Tone (2004) and Zhou et al. (2006) further extend the model by incorporating undesirable outputs.

  2. It means that covariates (environmental variables) do not affect the production process (input and output variables). Simar and Wilson (2007, 2011) show that the traditional two-stage approach estimates efficiency scores using input and output variables while ignoring covariates, and then regresses the estimated scores against covariates, is not well-defined and unmeaningful if the separability condition does not hold.

  3. Eco-efficiency is achieved through the delivery of “competitively-priced goods and services that satisfy human needs and bring quality of life, while progressively reducing environmental impacts and resource intensity throughout the life cycle, to a level at least in line with the earth’s estimated carrying capacity.”.

  4. It is a little different from Halkos and Tzeremes (2014) in which the joint probability of \((X,Y,B)\) conditional on Z = z defined as \(H_{XYB|Z} (x,y,b|z) = prob(X \le x,Y \ge y,B \ge b|Z = z)\). It can be seen that the definition of Halkos and Tzeremes (2014) deviates from the principle that the less undesirable outputs are generated, more efficient the production is.

  5. Regarding the selection of the bandwidth, Bădin et al. (2010) and De Witte and Kortelainen (2009) adapt the data-driven approach based on the least squares cross-validation (LSCV) method (Hall et al.,2004; Li and Racine, 2007) to estimate the best bandwidths. In our empirical study, we adapt the R codes kindly provided by Prof. De Witte to calculate the best bandwidths. We greatly appreciate Prof. De Witte for sharing their R codes.

  6. That is to say, the local coefficients of the explanatory variables are equal to zero almost everywhere. More technical details may be found in De Witte and Kortelainen (2009).

  7. The estimation can be easily conducted by the R package “np” which is developed by Hayfield and Racine (2008).

  8. More technical details can be found in De Witte and Kortelainen (2009).

  9. Data on GDP have been converted into the constant price level in 2005 using the GDP deflator index. Data on capital stock are estimated by the Perpetual Inventory Method (PIM), i.e., \(K_{t} = (1 - \delta )K_{t - 1} + I_{t}\) where \(\delta\) is the depreciation rate, and It is the investment measured in the constant price. The investment price index is used to deflate the nominal investment data. More details can be found in Inklaar and Timmer (2013).

  10. The developing countries involved in this study are mainly determined by data availability. Additionally, countries that are primarily oil producers are omitted from the sample.

  11. It is worth pointing out that at a very early stage of opening trade (the ratio of international trade to GDP is less than 14.9%), it seems that international trade would improve eco-efficiency. However, the number of observations in this regime are quite few, accounting for about 1% of the whole sample. Thus, the relationship between international trade and eco-efficiency is dominated by the U shape.

  12. About 57.6% of observations lie in this regime.

  13. In literature, the stochastic frontier methods are generally integrated with production/cost functions, Shepard distance function, directional distance function. The application of the stochastic frontier methods in non-radial and non-directional measures is relatively rare in literature.

References

  • An, Q., Tao, X., Dai, B., & Li, J. (2020). Modified distance friction minimization model with undesirable output: An application to the environmental efficiency of China’s regional industry. Computational Economics, 55(4), 1047–1071.

    Article  Google Scholar 

  • Ang, B. W., & Xu, X. Y. (2013). Tracking industrial energy efficiency trends using index decomposition analysis. Energy Economics, 40, 1014–1021.

    Article  Google Scholar 

  • Arabi, B., Munisamy, S., Emrouznejad, A., & Shadman, F. (2014). Power industry restructuring and eco-efficiency changes: A new slacks-based model in Malmquist-Luenberger Index measurement. Energy Policy, 68, 132–145.

    Article  Google Scholar 

  • Bădin, L., Daraio, C., & Simar, L. (2010). Optimal bandwidth selection for conditional efficiency measures: A data-driven approach. European Journal of Operational Research, 173, 633–640.

    Article  MATH  Google Scholar 

  • Bădin, L., Daraio, C., & Simar, L. (2012). How to measure the impact of environmental factors in a nonparametric production model. European Journal of Operational Research, 223, 818–833.

    Article  MATH  Google Scholar 

  • Candau, F., & Dienesch, E. (2017). Pollution haven and corruption paradise. Journal of Environmental Economics and Management, 85, 171–192.

    Article  Google Scholar 

  • Chang, Y. T., Zhang, N., Danao, D., et al. (2013). Environmental efficiency analysis of transportation system in China: A non-radial DEA approach. Energy Policy, 58, 277–283.

    Article  Google Scholar 

  • Chen, S., Saud, S., Bano, S., & Haseeb, A. (2019). The nexus between financial development, globalization, and environmental degradation: Fresh evidence from Central and Eastern European Countries. Environmental Science and Pollution Research, 26(24), 24733–24747.

    Article  Google Scholar 

  • Chung, Y. H., Färe, R., & Grosskopf, S. (1997). Productivity and undesirable outputs: A directional distance function approach. Journal of Environmental Management, 51(3), 229–240.

    Article  Google Scholar 

  • Cole, M. A., & Elliott, R. J. R. (2003). Determining the trade–environment composition effect: The role of capital, labor and environmental regulations. Journal of Environmental Economics and Management, 46(3), 363–383.

    Article  MATH  Google Scholar 

  • Daraio, C., & Simar, L. (2005). Introducing environmental variables in nonparametric frontier models: A probabilistic approach. Journal of Productivity Analysis, 24, 93–121.

    Article  Google Scholar 

  • Daraio, C., & Simar, L. (2006). A robust nonparametric approach to evaluate and explain the performance of mutual funds. European Journal of Operational Research, 175, 516–542.

    Article  MathSciNet  MATH  Google Scholar 

  • Daraio, C., & Simar, L. (2007). Conditional nonparametric frontier models for convex and nonconvex technologies: A unifying approach. Journal of Productivity Analysis, 28, 13–32.

    Article  Google Scholar 

  • Daraio, C., & Simar, L. (2014). Directional distances and their robust versions: Computational and testing issues. European Journal of Operational Research, 237, 358–369.

    Article  Google Scholar 

  • De Witte, K., Kortelainen, M., 2009. Blaming the exogenous environment? Conditional efficiency estimation with continuous and discrete exogenous variables. MPRA Paper. Munich.

  • Du, K., Huang, L., & Yang, Z. (2015). Understanding industrial energy productivity growth in China: A production-theoretical approach. Energy Efficiency, 8(3), 493–508.

    Article  Google Scholar 

  • Dyckhoff, H., & Allen, K. (2001). Measuring ecological efficiency with data envelopment analysis (DEA). European Journal of Operational Research, 132(2), 312–325.

    Article  MATH  Google Scholar 

  • Emrouznejad, A., Anouze, A. L., & Thanassoulis, E. (2010). A semi-oriented radial measure for measuring the efficiency of decision making units with negative data, using DEA. European Journal of Operational Research, 200(1), 297–304.

    Article  MATH  Google Scholar 

  • Farooq, S., Ozturk, I., Majeed, M. T., & Akram, R. (2022). Globalization and CO2 emissions in the presence of EKC: A global panel data analysis. Gondwana Research, 106, 367–378.

    Article  Google Scholar 

  • Feenstra, R. C., Inklaar, R., & Timmer, M. (2015). The next generation of the Penn World Table. American Economic Review, 105(10), 3150–3182.

    Article  Google Scholar 

  • Ghani, G. M. (2012). Does trade liberalization effect energy consumption? Energy Policy, 43, 285–290.

    Article  Google Scholar 

  • Grossman, G. M., & Helpman, E. (1990). Trade, innovation, and growth. American Economic Review, 80(2), 86–91.

    Google Scholar 

  • Hakimi, A., & Hamdi, H. (2016). Trade liberalization, FDI inflows, environmental quality and economic growth: A comparative analysis between Tunisia and Morocco. Renewable and Sustainable Energy Reviews, 58, 1445–1456.

    Article  Google Scholar 

  • Halkos, G. E., & Tzeremes, N. G. (2014). Public sector transparency and countries’ environmental performance: A nonparametric analysis. Resource and Energy Economics, 38, 19–37.

    Article  Google Scholar 

  • Hall, P., Racine, J., & Li, Q. (2004). Cross-validation and the estimation of conditional probability densities. Journal of the American Statistical Association, 99, 1015–1026.

    Article  MathSciNet  MATH  Google Scholar 

  • Hayfield, T., & Racine, J. S. (2008). Nonparametric econometrics: The np package. Journal of Statistical Software, 27, 1–32.

    Article  Google Scholar 

  • Honma, S. (2015). Does international trade improve environmental efficiency? An application of a super slacks-based measure of efficiency. Journal of Economic Structures, 4(1), 13.

    Article  Google Scholar 

  • Huang, J., Yang, X., Cheng, G., & Wang, S. (2014). A comprehensive eco-efficiency model and dynamics of regional eco-efficiency in China. Journal of Cleaner Production, 67, 228–238.

    Article  Google Scholar 

  • Inklaar, R., Timmer, M. (2013). Capital, labor and TFP in PWT 8.0. Available at <https://www.rug.nl/ggdc/productivity/pwt/related-research-papers/capital_labor_and_tfp_in_pwt80.pdf> [Acessed 1.28.2020]

  • Iram, R., Zhang, J., Erdogan, S., Abbas, Q., & Mohsin, M. (2020). Economics of energy and environmental efficiency: Evidence from OECD countries. Environmental Science and Pollution Research, 27(4), 3858–3870.

    Article  Google Scholar 

  • Karakaplan, M. U., & Kutlu, L. (2017). Endogeneity in panel stochastic frontier models: An application to the Japanese cotton spinning industry. Applied Economics, 49(59), 5935–5939.

    Article  Google Scholar 

  • Ke, T Y., 2017. Energy efficiency of APEC members–applied dynamic SBM model. Carbon Management, 1–11.

  • Kemfert, C. (2014). Climate coalitions and international trade: Assessment of cooperation incentives by issue linkage. Energy Policy, 32(4), 455–465.

    Article  Google Scholar 

  • Korhonen, P. J., & Luptacik, M. (2004). Eco-efficiency analysis of power plants: An extension of data envelopment analysis. European Journal of Operational Research, 154(2), 437–446.

    Article  MATH  Google Scholar 

  • Le, T. H., Chang, Y., & Park, D. (2016). Trade openness and environmental quality: International evidence. Energy Policy, 92, 45–55.

    Article  Google Scholar 

  • Letchumanan, R., & Kodama, F. (2000). Reconciling the conflict between the “pollution-haven” hypothesis and an emerging trajectory of international technology transfer. Research Policy, 29(1), 59–79.

    Article  Google Scholar 

  • Li, Q., & Racine, J. S. (2007). Nonparametric econometrics: Theory and practice. Princeton University Press.

    MATH  Google Scholar 

  • Managi, S., Hibiki, A., & Tsurumi, T. (2009). Does trade openness improve environmental quality? Journal of Environmental Economics and Management, 58(3), 346–363.

    Article  Google Scholar 

  • Mastromarco, C., & Simar, L. (2015). Effect of FDI and time on catching up: New Insights from a conditional nonparametric frontier analysis. Journal of Applied Econometrics, 30(5), 826–847.

    Article  MathSciNet  Google Scholar 

  • Mavi, N. K., & Mavi, R. K. (2019). Energy and environmental efficiency of OECD countries in the context of the circular economy: Common weight analysis for malmquist productivity index. Journal of Environmental Management, 247, 651–661.

    Article  Google Scholar 

  • Morita, H., Hirokawa, K., & Zhu, J. (2005). A slack-based measure of efficiency in context-dependent data envelopment analysis. Omega, 33(4), 357–362.

    Article  Google Scholar 

  • Ohene-Asare, K., Tetteh, E. N., & Asuah, E. L. (2020). Total factor energy efficiency and economic development in Africa. Energy Efficiency, 13(6), 1177–1194.

    Article  Google Scholar 

  • Omri, A., Daly, S., Rault, C., & Chaibi, A. (2015). Financial development, environmental quality, trade and economic growth: What causes what in MENA countries. Energy Economics, 48, 242–252.

    Article  Google Scholar 

  • Ouyang, X., Chen, J., & Du, K. (2021). Energy efficiency performance of the industrial sector: From the perspective of technological gap in different regions in China. Energy, 214, 118865.

    Article  Google Scholar 

  • Park, Y. S., Lim, S. H., Egilmez, G., & Szmerekovsky, J. (2018). Environmental efficiency assessment of US transport sector: A slack-based data envelopment analysis approach. Transportation Research Part d: Transport and Environment, 61, 152–164.

    Article  Google Scholar 

  • Ren, S., Yuan, B., Ma, X., et al. (2014). International trade, FDI (foreign direct investment) and embodied CO2 emissions: A case study of China’s industrial sectors. China Economic Review, 28, 123–134.

    Article  Google Scholar 

  • Saud, S., Chen, S., & Haseeb, A. (2020). The role of financial development and globalization in the environment: Accounting ecological footprint indicators for selected one-belt-one-road initiative countries. Journal of Cleaner Production, 250, 119518.

    Article  Google Scholar 

  • Schmidheiny, S. (1992). Changing Course: A Global Business Perspective on Development and the Environment. MIT Press.

    Google Scholar 

  • Shahbaz, M., Hye, Q. M. A., Tiwari, A. K., et al. (2013a). Economic growth, energy consumption, financial development, international trade and CO2 emissions in Indonesia. Renewable and Sustainable Energy Reviews, 25, 109–121.

    Article  Google Scholar 

  • Shahbaz, M., Tiwari, A. K., & Nasir, M. (2013b). The effects of financial development, economic growth, coal consumption and trade openness on CO2 emissions in South Africa. Energy Policy, 61, 1452–1459.

    Article  Google Scholar 

  • Shen, J. (2008). Trade liberalization and environmental degradation in China. Applied Economics, 40(8), 997–1004.

    Article  Google Scholar 

  • Simar, L., & Vanhems, A. (2012). Probabilistic characterization of directional distances and their robust versions. Journal of Econometrics, 166, 342–354.

    Article  MathSciNet  MATH  Google Scholar 

  • Simar, L., & Wilson, P. W. (2007). Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics, 136, 31–64.

    Article  MathSciNet  MATH  Google Scholar 

  • Simar, L., & Wilson, P. W. (2011). Two-stage DEA: Caveat emptor. Journal of Productivity Analysis, 36, 205–218.

    Article  Google Scholar 

  • Somanathan, E. (2010). Effects of information on environmental quality in developing countries. Review of Environmental Economics and Policy, 4(2), 275–292.

    Article  Google Scholar 

  • Song, M., Zhou, Y. (2015). Quantitative analysis of foreign trade and environmental efficiency in China. Emerging Markets Finance and Trade, 1–14.

  • Tang, J., Tang, L., Li, Y., & Hu, Z. (2020). Measuring eco-efficiency and its convergence: Empirical analysis from China. Energy Efficiency, 13(6), 1075–1087.

    Article  Google Scholar 

  • Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130, 498–509.

    Article  MathSciNet  MATH  Google Scholar 

  • Tone, K. (2004). Dealing with undesirable outputs in DEA: A slacks-based measure (SBM) approach. In: Presentation at NAPW III, Toront.

  • Wang, S., Sun, X., & Song, M. (2021). Environmental regulation, resource misallocation, and ecological efficiency. Emerging Markets Finance and Trade, 57(3), 410–429.

    Article  Google Scholar 

  • Wei, Y., Li, Y., Wu, M., & Li, Y. (2020). Progressing sustainable development of “the Belt and Road countries”: Estimating environmental efficiency based on the Super-slack-based measure model. Sustainable Development, 28(4), 521–539.

    Article  Google Scholar 

  • Xu, S. C., Zhang, W. W., He, Z. X., Han, H. M., Long, R. Y., & Chen, H. (2017). Decomposition analysis of the decoupling indicator of carbon emissions due to fossil energy consumption from economic growth in China. Energy Efficiency, 10(6), 1365–1380.

    Article  Google Scholar 

  • Yanikkaya, H. (2003). Trade openness and economic growth: A cross-country empirical investigation. Journal of Development Economics, 72(1), 57–89.

    Article  Google Scholar 

  • Yu, Y., Hu, H., Zhang, Y., & Yin, Z. (2019). Metafrontier eco-efficiency and its convergence analysis for China: A multidimensional heterogeneity perspective. Emerging Markets Finance and Trade, 55(7), 1531–1549.

    Article  Google Scholar 

  • Zaidi, S. A. H., Zafar, M. W., Shahbaz, M., & Hou, F. (2019). Dynamic linkages between globalization, financial development and carbon emissions: Evidence from Asia Pacific Economic Cooperation countries. Journal of Cleaner Production, 228, 533–543.

    Article  Google Scholar 

  • Zhang, J., Zeng, W., & Shi, H. (2016). Regional environmental efficiency in China: Analysis based on a regional slack-based measure with environmental undesirable outputs. Ecological Indicators, 71, 218–228.

    Article  Google Scholar 

  • Zhou, P., Ang, B., & Poh, K. (2006). Slacks-based efficiency measures for modeling environmental performance. Ecological Economics, 60, 111–118.

    Article  Google Scholar 

  • Zhou, P., Ang, B. W., & Wang, H. (2012). Energy and CO2 emission performance in electricity generation: A non-radial directional distance function approach. European Journal of Operational Research, 221(3), 625–635.

    Article  MATH  Google Scholar 

Download references

Acknowledgements

We acknowledge the funding support from the National Natural Science Foundation of China (Nos. 72074184, 71772065), Shanghai Science and Technology Plan Project (22692110000, 21692107200), and the Fundamental Research Funds for the Central Universities at East China Normal University (Grand Nos. 2021ECNU-YYJ026, 2021QKT007) and the Fundamental Research Funds for the Central Universities at Xiamen University (Grand No. 20720201016).

Author information

Authors and Affiliations

  1. School of Management, China Institute for Studies in Energy Policy, Xiamen University, Xiamen, 361005, China

    Kerui Du & Yiqian Sun

  2. School of Economics, Faculty of Economics and Management, East China Normal University, Shanghai, 200062, China

    Xiaoling Ouyang

Authors
  1. Kerui Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaoling Ouyang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yiqian Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaoling Ouyang.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's note

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (ZIP 172 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Du, K., Ouyang, X. & Sun, Y. Does international trade contribute to eco-efficiency performance improvement? Evidence from the emerging and developing economies. Energy Efficiency 15, 60 (2022). https://doi.org/10.1007/s12053-022-10067-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12053-022-10067-4

Keywords

Search

Navigation