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Technical Change and Green Productivity

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Abstract

When technologies could be “dirty” and “clean” in the context of green development, technical change does not necessarily mean (green) productivity growth. This paper studies the nonlinear impact of technical change on green productivity in China by applying a panel smooth transition regression approach with a panel data set of 284 prefecture-level cities from 2004 to 2015. Green productivity is measured by the meta-frontier Malmquist–Luenberger productivity growth (MML) index. Technical change is considered in three dimensions: indigenous technical change indicated by the stock of knowledge based on patents, technology transfers from foreign direct investment (FDI), and absorptive capacity. We find a non-linear relationship between technical change and green productivity contingent on specific economic situations and the city’s endowment of natural resources. In general, indigenous technical change shows an adverse effect on green productivity in China, which is much more prominent in the resource-dependent cities than in the non-resource-dependent cities. Technology transfers from FDI may either improve or hinder green productivity growth as economic situations change, while absorptive capacity has a small but positive effect. Also, these two effects are affected by the city’s endowment of natural resources. Accordingly, we discuss some policy implications.

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Notes

  1. More environmental devastations are proved to be attributed to human activity, for instance, the European Heat Wave of 2003 (Stott et al. 2004), sea-level rise (Nicholls and Lowe 2006).

  2. This point is addressed by the former Premier Wen Jiabao in March 2007 and repeated for several times ever since. Note that China is now the largest energy consumer and air pollution emitter in the world.

  3. The World Bank, 2012. China 2030: Building a modern, harmonious, and creative society. World Bank Publications.

  4. See the report of the 19th National Congress of the Communist Party of China in 2017.

  5. In this regard, there is a debate about whether and when this transformation happened in China by using various methods for calculating productivity such as Solow residuals. See a survey in Chen et al. (2011).

  6. The theoretical literature of directed technical change is full-fledged, while empirical evidence remains scarce. See a survey in Aghion et al. (2016).

  7. The “dirty” technology is associated with the polluting non-renewable sources such as fossil fuels, while the “clean“ one is associated with renewable or clean inputs such as labor according to the literature in directed technical change (Acemoglu 2002). The direction of technical change may be affected by energy price (Popp 2002; Hassler et al. 2012), the relative size of different inputs (Acemoglu and Linn 2004; Acemoglu et al. 2012), and history of innovations (Aghion et al. 2016).

  8. Most of existing studies decompose the green productivity into technical change and production efficiency change, or additionally scale efficiency change, embedded in the Malmquist–Luenberger index (Chung et al. 1997; Wang et al. 2010; Lin et al. 2013; Choi et al. 2015; Liu et al. 2016; Tian and Lin 2017). Besides that, other studies also apply empirical regressions and identify other factors influencing green productivity, such as environmental regulation (Zhang et al. 2011; Xie et al. 2017), structural transformation (Chen and Golley 2014; Li and Lin 2017).

  9. Indeed, many cities in China have initiated the process of green transformations. There are also some examples of successful transformations like Tokyo, Stuttgart, Munich, Seoul, Seattle, and Toulouse. See more in the World Bank China 2030 report (2012).

  10. Concerning the theoretical foundation for our estimation, one can combine a classical model of R&D model on productivity (Ugur et al. 2016) with directed technical change (Acemoglu et al. 2012): \({\text{GTFP}}=F(R_c, R_d,Z)\), with \(R_{c}\) and \(R_d\) being R&D on clean technologies and dirty ones, \(\partial {\text{GTFP}}/\partial R_c>0\), \(\partial {\text{GTFP}}/\partial R_d<0\), and Z are the other variables affecting green TFP. Therefore, technology development directed to “clean” technologies will increase green TFP, while that to “dirty” ones will do otherwise. Due to data unavailability, we cannot directly estimate the effects of dirty technologies and clean ones on green TFP as what Aghion et al. (2016) do in auto industry. However, the estimated sign of technology development on green TFP is the combined effect of both types of technologies, indicating which type of technologies is in the dominant position.

  11. The same treatment is used by Aghion et al (2016, p. 8) who “lag prices and knowledge stocks to reflect delayed response and to mitigate contemporaneous feedback effects”.

  12. This index is obtained based on the pioneering work of Chung et al. (1997). Other similar variations are also employed in different cases (e.g., Zhang et al. 2011; Lin et al. 2013; Chen and Golley 2014).

  13. See Oh (2010) for the calculation and discussion of the MML index in details.

  14. Note also that patents are no perfect measure of technical change since the values of different patents vary.

  15. Consistent with the literature, the knowledge stocks are constructed by the perpetual inventory method. Note also that it is difficult to measure the rate of decay of knowledge directly, and thus two indirect methods are commonly used instead: the number of patents survived over time and the reciprocal of the average life of the patent.

  16. The direct way of acquiring foreign technologies is simply purchasing from other developed countries, which is indicated by the government’s or firms’ expenditure for acquiring foreign technologies in some studies such as Li (2011) and Hu et al. (2005). But its data are only available at the province-level.

  17. Note also that the interaction term of human capital and FDI is also used as a proxy for absorptive capacity in some studies like Kneller (2005). In order to check the robustness of our results and discussing the role of human capital in taking in foreign technologies, this proxy (human × FDI) is also added into our specification in Subsection 4.4. The results confirm the robustness of our finding concerning absorptive capacity indicated by TD1*TD2.

  18. Due to the unavailability of the data of city-level capital accumulations, we use instead fixed capital stocks as with Hall and Jones (1999). The calculations are:

    $$\begin{aligned}&I_{2003}=I/(\delta +g)\\&K_{it+1}=K_{it}(1-\delta )+I_it \end{aligned}$$

    where \(I_{2003}\) is the fixed capital stock at the base year of 2003, I is the fixed capital at 2003, \(\delta\) is the depreciation rate, and g is the average annual growth rate over 2003 to 2015. \(K_{it+1}\) and \(K_{it}\) are fixed capital stock at time \(t+1\) and t. By using the perpetual inventory method, we obtain the data of city-level fixed capital stock and then deflate it by the fixed assets investment index.

  19. The emissions of \({\text{CO}}_2\) are not used because of the unavailability of the city-level data.

  20. human\(\times\)FDI serves our purpose for studying whether the city’s endowment of natural resources affects the technology-green productivity nexus in the next section.

  21. Note that the original data source is the official Statistical Communiqués of all prefecture-level cities. See http://www.tjcn.org.

  22. Note that all empirical results are estimated by the WinRats 10.0 with the RATS code provided by Colletaz G., http://www.univ-orleans.fr/deg/masters/ESA/GC/gcolletaz_R.htm.

  23. For the procedure of determining the number of regimes in details, please see Gonzalez et al. (2005, p. 11).

  24. Furthermore, we also run the regressions of ordinary least squares and fixed effects, and find that the estimated coefficients by these two methods are much biased. The results are available from the author upon request.

  25. See the report by the World Bank, 2012. China 2030: Building a modern, harmonious, and creative society. World Bank Publications.

  26. See the thread of literature supporting positive results such as Rhee and Belot (1989), Aitken and Harrison (1999), Smarzynska Javorcik (2004), and the other thread for the negative results in Haddad and Harrison (1993), Liu (2008), and so forth. A survey of the literature on technology transfers from FDI at the firm-level is done by Gorg and Strobl (2001).

  27. 71% of China’s middle-class consumers respond that they had been spending more on green products according to a survey of the Hong Kong Trade Development Council in 2017. See http://economists-pick-research.hktdc.com/business-news/article/Research-Articles/China-s-Middle-Class-Consumers-Attitudes-towards-Green-Consumption/rp/en/1/1X000000/1X0AB4QY.htm.

  28. The Pollution Haven Hypothesis claims that companies who choose to invest in foreign countries prefer to (re)locate to the countries with the comparatively lower environmental regulation or weaker enforcement, which is extensively studied in the literature such as Eskeland and Harrison (2002), Cole and Elliott (2005), and Levinson and Taylor (2008).

  29. The city’s endowment is important because Acemoglu (2002) and Acemoglu et al. (2012) show theoretically that the relative abundance of inputs may determine the direction of technical change, namely, the market size effect in their context encouraging technical change favoring abundant factors. Also, this effect is empirically testified in the pharmaceutical industry (Acemoglu and Linn 2004).

  30. Note although that the estimate of TD1 in Model 1 is positive in the first regime, but it is significantly negative in the second regime. The reason might be that in the non-resource dependent with lower per capita income many clean technologies are innovated in the agriculture or light industry.

  31. We thank the reviewer for motivating us to add this procedure. Note that only the results of regressions with the full sample are reported below. The results with the two subsamples are also robust, which are available from the authors upon request.

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Acknowledgements

We thank the editor, Robert Elliott, and the referee for their valuable comments and suggestions. Peng Li thanks the financial support provided by the Postdoctoral Research Foundation of China (CN) (2019M650945).

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  1. Chinese Academy of Social Sciences, Institute of Industrial Economics, Beijing, 100836, China

    Peng Li

  2. Chinese Academy of Social Sciences, Institute of Economics, No.2 Yuetanbeixiaojie, Xicheng District, Beijing, 100836, China

    Yaofu Ouyang

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Li, P., Ouyang, Y. Technical Change and Green Productivity. Environ Resource Econ 76, 271–298 (2020). https://doi.org/10.1007/s10640-020-00424-1

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