Skip to main content
Log in

Green productivity evolution under non-convex environmental technology

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

Abstract

Most of empirical studies assume convex production technology to analyze productivity growth at the aggregate level. However, convexity assumption implies benchmarking against production plans that are not empirically observed. Unlike previous studies, we adopt a non-convex approach based on observed input–output combinations, which requires minimal assumptions in terms of production technology. Incorporating energy and carbon emissions into the production function, this paper investigates green growth and its driving forces among 39 European countries over 1991–2019. A by-production technology is applied alongside the environmental Luenberger-Hicks-Moorsteen (LHM) productivity indicator and directional distance functions. The results show that the production technology has been expanding (i.e., technical progress), whereas scale inefficiency led to the negative green total factor productivity (TFP) growth in Europe. At the group level, green TFP in OECD countries tends to increase, whereas a sharp decline is observed for non-OECD countries. Furthermore, we note that human capital, R&D intensity, energy consumption structure, and urbanization level all have significant positive impacts on green TFP growth in Europe, especially for non-OECD countries. The corresponding policy implications are derived to promote regional cooperation and technology sharing.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data Availability

Data will be made available on request.

Notes

  1. 91/565/EEC: Council Decision of 29 October 1991 concerning the promotion of energy efficiency in the Community.

References

  • Adeli Nik, H., Sattari Nasab, Z., Salmani, Y., & Shahriari, N. (2013). The relationship between financial development indicators and human capital in Iran. Management Science Letters, 3(4), 1261–1272.

    Article  Google Scholar 

  • Aigner, D., Lovell, C. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6(1), 21–37.

    Article  MathSciNet  MATH  Google Scholar 

  • Almeida, P., & Kogut, B. (1999). Localization of knowledge and the mobility of engineers in regional networks. Management Science, 45(7), 905–917.

    Article  Google Scholar 

  • Ang, F., & Kerstens, P. J. (2017). Decomposing the Luenberger–Hicks–Moorsteen total factor productivity indicator: An application to US agriculture. European Journal of Operational Research, 260(1), 359–375.

    Article  MathSciNet  MATH  Google Scholar 

  • Ang, J. B., Madsen, J. B., & Islam, M. R. (2011). The effects of human capital composition on technological convergence. Journal of Macroeconomics, 33(3), 465–476.

    Article  Google Scholar 

  • Baležentis, T., Kerstens, K., & Shen, Z. (2017). An environmental Luenberger– Hicks–Moorsteen Total factor productivity indicator for OECD countries. Working Papers 2017-EQM-02.

  • Benhabib, J., & Spiegel, M. M. (1994). The role of human capital in economic development evidence from aggregate cross-country data. Journal of Monetary Economics, 34(2), 143–173.

    Article  Google Scholar 

  • Bjurek, H. (1996). The Malmquist total factor productivity index. The Scandinavian Journal of Economics, 98, 303–313. https://doi.org/10.2307/3440861

    Article  Google Scholar 

  • Briec, W., & Kerstens, K. (2004). A Luenberger-Hicks-Moorsteen productivity indicator: Its relation to the Hicks-Moorsteen productivity index and the Luenberger productivity indicator. Economic Theory, 23(4), 925–939.

    Article  MathSciNet  MATH  Google Scholar 

  • Cattaneo, C. (2019). Internal and external barriers to energy efficiency: which role for policy interventions. Energy Efficiency, 12(5), 1293–1311.

    Article  Google Scholar 

  • Caves, D. W., Christensen, L. R., & Diewert, W. E. (1982). The economic theory of index numbers and the measurement of input, output, and productivity. Econometrica: Journal of the Econometric Society, 1393–1414.

  • Chambers, R. G. (2002). Exact nonradial input, output, and productivity measurement. Economic Theory, 20(4), 751–765.

    Article  MathSciNet  MATH  Google Scholar 

  • Chambers, R. G., Fāure, R., & Grosskopf, S. (1996). Productivity growth in APEC countries. Pacific Economic Review, 1(3), 181–190.

    Article  Google Scholar 

  • Chen, S. (2015). The evaluation indicator of ecological development transition in China’s regional economy. Ecological Indicators, 51, 42–52.

    Article  Google Scholar 

  • Chen, G., & Hu, J. (2019). The impact of human capital accumulation on environmental quality. Urban Problems, 10, 46–52.

    Google Scholar 

  • Cheng, Z., Li, L., & Liu, J. (2021). Research on China’s industrial green biased technological progress and its energy conservation and emission reduction effects. Energy Efficiency, 14(5), 42.

    Article  Google Scholar 

  • De Borger, B., Kerstens, K., Moesen, W., & Vanneste, J. (1994). A non-parametric free disposal hull (FDH) approach to technical efficiency: An illustration of radial and graph efficiency measures and some sensitivity results. Swiss Journal of Economics and Statistics, 130(4), 647–667.

    Google Scholar 

  • Deprins, D. L. Simar, & Tulkens, H. (1984). Measuring labor efficiency on post offices. In M. Marchand, P. Pestieau & H. Tulkens (Eds.), The performance of public enterprises: Concepts and measurement, (pp. 243–267). Amsterdam.

  • Ding, L., Wu, M., Jiao, Z., & Nie, Y. (2021). The positive role of trade openness in industrial green total factor productivity—provincial evidence from China. Environmental Science and Pollution Research, 1–14.

  • Dong, B., Xu, Y., & Fan, X. (2020). How to achieve a win-win situation between economic growth and carbon emission reduction: Empirical evidence from the perspective of industrial structure upgrading. Environmental Science and Pollution Research, 27(35), 43829–43844.

    Article  Google Scholar 

  • Du, K., & Li, J. (2019). Towards a green world: How do green technology innovations affect total-factor carbon productivity. Energy Policy, 131, 240–250.

    Article  Google Scholar 

  • Du, L., Wei, C., & Cai, S. (2012). Economic development and carbon dioxide emissions in China: Provincial panel data analysis. China Economic Review, 23(2), 371–384.

    Article  Google Scholar 

  • Färe, R., Grosskopf, S., & Pasurka, C. A., Jr. (2001). Accounting for air pollution emissions in measures of state manufacturing productivity growth. Journal of Regional Science, 41(3), 381–409.

    Article  Google Scholar 

  • Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, Series A, 120, 253–281.

    Article  Google Scholar 

  • Feng, T., Sun, L., & Zhang, Y. (2009). The relationship between energy consumption structure, economic structure and energy intensity in China. Energy Policy, 37(12), 5475–5483.

    Article  Google Scholar 

  • Gupta, J., & Ivanova, A. (2009). Global energy efficiency governance in the context of climate politics. Energy Efficiency, 2, 339.

    Article  Google Scholar 

  • Johnes, J. (2006). Data envelopment analysis and its application to the measurement of efficiency in higher education. Economics of Education Review, 25(3), 273–288.

    Article  MATH  Google Scholar 

  • Jorgenson, A. K. (2009). Foreign direct investment and the environment, the mitigating influence of institutional and civil society factors, and relationships between industrial pollution and human health: A panel study of less-developed countries. Organization & Environment, 22(2), 135–157.

    Article  Google Scholar 

  • Lin, B., & Chen, Z. (2018). Does factor market distortion inhibit the green total factor productivity in China? Journal of Cleaner Production, 197, 25–33.

    Article  Google Scholar 

  • Mavi, R. K., Mavi, N. K., Saen, R. F., & Goh, M. (2022). Common weights analysis of renewable energy efficiency of OECD countries. Technological Forecasting and Social Change, 185, 122072.

    Article  Google Scholar 

  • Murshed, M. (2020). Are Trade Liberalization policies aligned with Renewable Energy Transition in low and middle income countries? An Instrumental Variable approach. Renewable Energy, 151, 1110–1123.

    Article  Google Scholar 

  • Murshed, M., & Alam, M. S. (2021). Estimating the macroeconomic determinants of total, renewable, and non-renewable energy demands in Bangladesh: the role of technological innovations. Environmental Science and Pollution Research, 1–21.

  • Murty, S., & Russell, R. R. (2002). On modeling pollution generating technologies (pp. 1–18). Department of Economics, University of California.

    Google Scholar 

  • Murty, S., Russell, R. R., & Levkoff, S. B. (2012). On modeling pollution-generating technologies. Journal of Environmental Economics and Management, 64(1), 117–135.

    Article  Google Scholar 

  • O’Donnell, C. J. (2012). An aggregate quantity framework for measuring and decomposing productivity change. Journal of Productivity Analysis, 38(3), 255–272.

    Article  Google Scholar 

  • Qiang, Z. H. E. N. G. (2018). The influence of urbanization on green total factor productivity: an analysis based on the threshold effect of public expenditure. Urban Problems, 03.

  • Ramos, R., Surinach, J., & Artís, M. (2012). Regional economic growth and human capital: The role of over-education. Regional Studies, 46(10), 1389–1400.

    Article  Google Scholar 

  • Ray, S. C., Mukherjee, K., & Venkatesh, A. (2018). Nonparametric measures of efficiency in the presence of undesirable outputs: A by-production approach. Empirical Economics, 54(1), 31–65.

    Article  Google Scholar 

  • Schultz, T. W. (1960). Capital formation by education. Journal of Political Economy, 68(6), 571–583.

    Article  Google Scholar 

  • Schultz, T. W. (1961). Investment in human capital. The American Economic Review, 51(1), 1–17.

    Google Scholar 

  • Solow, R. M. (1957). Technical change and the aggregate production function. The Review of Economics and Statistics, 312–320.

  • Song, M., & Li, H. (2020). Total factor productivity and the factors of green industry in Shanxi Province, China. Growth and Change, 51(1), 488–504.

    Article  Google Scholar 

  • Song, M., Wang, S., & Sun, J. (2018). Environmental regulations, staff quality, green technology, R&D efficiency, and profit in manufacturing. Technological Forecasting and Social Change, 133, 1–14.

    Article  Google Scholar 

  • Stroombergen, A., Rose, W. D., & Nana, G. (2002). Review of the statistical measurement of human capital. Statistics New Zealand.

    Google Scholar 

  • Teng, M., & Shen, M. (2023). Fintech and energy efficiency: Evidence from OECD countries. Resources Policy, 82, 103550.

    Article  Google Scholar 

  • Wang, M., Xu, M., & Ma, S. (2021). The effect of the spatial heterogeneity of human capital structure on regional green total factor productivity. Structural Change and Economic Dynamics, 59, 427–441.

    Article  Google Scholar 

  • Wu, H., Ren, S., Yan, G., & Hao, Y. (2020). Does China’s outward direct investment improve green total factor productivity in the “Belt and Road” countries? Evidence from dynamic threshold panel model analysis. Journal of Environmental Management, 275, 111295.

    Article  Google Scholar 

  • Zhang, J., Lu, G., Skitmore, M., & Ballesteros-Pérez, P. (2021). A critical review of the current research mainstreams and the influencing factors of green total factor productivity. Environmental Science and Pollution Research, 1–14.

Download references

Acknowledgements

This research was supported by the National Natural Science Foundation of China (71973011 and 72104028).

Author information

Authors and Affiliations

Authors

Contributions

Cai J. and Xu X. contribute equally to this paper and share the first co-authorship. Introduction and Literature review, Cai J. and Xu X.; Method and data, Cai J. and Shen Z.Y.; Empirical results and discussion, Cai J. and Balezentis T.; Conclusions and discussions, Shen Z.Y. and Xu X.; Writing–Original Draft Preparation, Xu X.; Writing–Review and Editing, Balezentis T. and Shen Z.Y. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Z. Y. Shen.

Ethics declarations

Conflict of interest

None.

Additional information

Publisher's note

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

Highlights

• The free disposal hull is adopted to construct best practice frontier.

• Green total factor productivity of European countries is calculated using LHM indicator.

• Human capital has an impact on green total factor productivity in Europe.

• Green total factor productivity is different between OECD and non-OECD countries.

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

Cai, J., Xu, X., Balezentis, T. et al. Green productivity evolution under non-convex environmental technology. Energy Efficiency 16, 59 (2023). https://doi.org/10.1007/s12053-023-10136-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12053-023-10136-2

Keywords

Navigation