Elsevier

Energy Economics

Volume 90, August 2020, 104842
Energy Economics

Do renewable energy technology innovations promote China's green productivity growth? Fresh evidence from partially linear functional-coefficient models

https://doi.org/10.1016/j.eneco.2020.104842Get rights and content

Highlights

  • The PLFC model is employed to explore the heterogeneous effects of RETI.

  • The income level shapes the relationship between RETI and green productivity.

  • The positive role of RETI is found in China's well-developed provinces.

Abstract

Renewable energy technology innovation can benefit the environment by promoting green productivity, as proposed by existing theoretical studies. However, recent uneven developments of both environmental performance and renewable energy technology among regions in China remind us to revisit the above theoretical link. In this paper, we relax the hypothesized homogeneity and linearity in traditional empirical models to investigate the effects of renewable energy technology innovation on China's green productivity. The results of the partially linear functional-coefficient models show that the effect of renewable energy technological innovation on green productivity is significant only when the relative income level of a province passes a critical turning point. Beyond the turning point, such an effect increases with the growth of relative income levels. Finally, we provide provincial specific policy implications based on the estimated nonparametric relationship between renewable energy technology innovation and green productivity.

Introduction

The Sustainable Development Goals (SDGs) initiated by the United Nations have set significant challenges, both in theory and in practice, to the world economy. The driving factors and regulatory frameworks of pollution emissions have gained sustained attention in the past decades (Carrión-Flores and Innes, 2010; Costantini et al., 2013; de Vries and Ferrarini, 2017; Du et al., 2019a; Gilli et al., 2014; Guan et al., 2018; Wang et al., 2019a). Nevertheless, the SDGs place a particular emphasis on environmentally inclusive growth, which entails curbing pollution emissions together with stimulating economic growth (Sinha et al., 2020). In this sense, how to promote green productivity growth is an urgent issue for most economies (Wang et al., 2019c). The empirical studies have intensively explored the roles of the economic scale, economic structure, technical progress, and governmental policy in changes of macroeconomic green productivity (Chen and Golley, 2014; Lin and Du, 2015b; Yang et al., 2017), and some studies highlighted the role of technology (Wang et al., 2018; Zhou et al., 2010). Notably, green technology innovations, including renewable energy technology innovations (RETI, henceforth), are regarded as fundamental driving forces of green productivity growth (Ghisetti and Quatraro, 2017).

However, the recent progress in China's green growth has raised some new issues on the relationship between RETI and green productivity. To realize the clean transition of the energy system it promised to the world, China has been investing a great number of resources in developing renewable energy technologies (Lin and Chen, 2019; Xu and Lin, 2018). From 2009 to 2018, its new investment in renewable energy accounted for nearly 30% of the global volume (REN21, 2019). As an output of the investment, the number of patent applications in renewable energy technology has been showing a rapidly rising trend (Lin and Zhu, 2019). However, whether the strengthening of RETI at the national level can spontaneously induce green productivity growth in all regions of China, two recently emerging issues make the answer full of uncertainty.

First, the capacities of RETI are distributed unevenly among regions together with a vast range of regional income levels (Lin and Chen, 2019), while the climate-change-mitigation mission entails coordinated contributions from all regions (Du et al., 2012; Zheng et al., 2019). As shown in Panel (A) of Fig. 1, some provinces with their per capita GDP less than 3000 US dollars are lack of RETI stock. Such statuses would make it hard to absorb or further develop the frontier technologies, which relies on substantial investments in both cutting-edge researches and training skilled workers (Dussaux et al., 2018; Griffith et al., 2004; Keller, 1996). Recent studies found that even the technological transfer from the importation of machinery or foreign direct investment entails a sound technology foundation of receivers, not to mention the indigenous innovations or imitations (Dussaux et al., 2018). Thus, can the laggards in terms of both economic development and RETI keep pace with the increasingly strict requirements of climate change mitigation, such a question faced by China is also a mirror of the challenge for the whole world.

Second, the link between RETI and green productivity seems to depend on income levels. As shown in Panel (B) of Fig. 1, although the low-income group of provinces gained the largest average advancement in RETI stock from 2010 to 2015, it only experienced the lowest group-mean improvement in green productivity measured by output per unit CO2 emission. In existing studies, the hypothetical positive relationship between RETI and green productivity often relies on several assumptions regarding RETI's impact mechanisms. Specifically, it is assumed that progress in RETI would sufficiently cause the diffusion and deployment of the technologies to have impacts on green productivity. Nevertheless, the more advanced but more costly renewable energy technologies now appear to be economic burdens for users in underdeveloped regions who generally have worse financial conditions compared with those in developed regions (Pfeiffer and Mulder, 2013; Verdolini and Galeotti, 2011). Moreover, the ‘double externalities’ inherent in the renewable energy technologies would further hinder the widespread deployment of the technologies in poor regions because of the awareness of free riders (Ley et al., 2016). Referring to Panel (B) of Fig. 1, the mechanism between RETI and green productivity might be weak for underdeveloped regions, and the significance or magnitude of RETI's effect on green productivity might depend on the status of economic development.

This paper aims to investigate the effects of RETI on green productivity, focusing on the heterogeneity and conditions of such effects. In this direction, many studies are grounded on the assumption that technological progress or technological innovation can lead to green productivity growth and focus on evaluating the magnitude of its positive impact (Chen and Golley, 2014; Wang et al., 2018). However, recent theories such as directed technical change have argued that relative progress in substitutional technologies towards different directions might have various productivity or output effects when the economy reaches equilibrium (Acemoglu et al., 2012). This theoretical argument intuitively triggers the hypothesis that green technologies, rather than dirty technologies, are the fundamental driving forces of green productivity growth (Du and Li, 2019; Ghisetti and Quatraro, 2017). For instance, Ghisetti and Quatraro (2017) noted that the link between environmental innovation and environmental performance is “too often hypothesized to be positive rather than proven,” and tested such a link in their seminal work. Although some empirical studies have verified the role of environmental innovation in promoting green productivity (Ghisetti and Quatraro, 2017; Gilli et al., 2014), there are still some implicit theoretical assumptions to be examined. For instance, from environmental innovation to diffusion, development, and finally taking its effect on green productivity, such a long and complex influential chain is typically assumed to be unrelated to some key economic variables (Du and Li, 2019). As shown by the facts in Fig. 1, more theory should be incorporated and carefully examined if we want to observe the explicit relationship between RETI and green productivity.

This paper is closely related to the work of Lin and Zhu (2019). In addition to their seminal findings, this paper contributes to the literature from the following aspects. First, we test if the green-productivity impact of RETI depends on the income level. To do this, we employ a new empirical framework, i.e., the partially linear functional-coefficient (PLFC, henceforth) panel data model, which allows us to view the impact of RETI as a nonparametric function of income level. Second, we measure the green productivity by a more comprehensive indicator, i.e., global Malmquist-Luenberger (GML) index. It matches the concept of green productivity that inherently means the limitation of bad outputs while the goods outputs and inputs keeping unchanged (Oh, 2010; Wang and Shen, 2016), rather than related studies calculating the output per unit pollution emissions, e.g., GDP/CO2. Third, we explore the heterogeneity among different regions, relaxing the traditional assumption that there is a linear and uniform relationship between RETI and green productivity in all regions. The methodological framework allows us to analyze the province-and-year specific impact of RETI, which is rich in information for understanding the real progress of climate change mitigation. Fourth, to our best knowledge, this paper is among the first to explore the relationship between China's environmental innovation and macroeconomic green productivity, primarily focusing on renewable energy technology.

The rest of this paper is structured as follows. The next section describes the method employed in this study and the data. In the third section, we show empirical analysis and results. The fourth and fifth section presents the policy implications and conclusions of this study, respectively.

Section snippets

The measurement of green productivity

This paper aims to evaluate the effects of green technology innovations on green productivity in China. The measurement of green productivity growth can provide valuable information to policymakers, and a variety of measures were used to estimate the productivity at the country, province, and industry level (Wang et al., 2018; Weber and Domazlicky, 2001).

Traditional productivity indexes focused on measuring marketable outputs relative to paid factors of production (Chung et al., 1997). The

Results of baseline models

Table 3 presents the results of the baseline models. The central to our research question is the influence of renewable energy technological innovations on green productivity. In Column (1), the most restrictive specification only includes the lagged value of renewable energy technological innovations. From Columns (2) to (6), we incorporate the lagged term of accumulated green productivity, GDP per capita, the proportion of the secondary industry, the proportion of coal in energy consumption

Discussion on policies to promote green productivity

In addition to the fresh evidence on the relationship between RETI and green productivity, the empirical framework of this paper also enables us to exploit the policies for promoting China's green productivity from a provincial perspective. Combining the data on accumulated green productivities, income levels, and estimated effects of RETI, we classify the 30 provinces into four groups, as shown in Fig. 5. The first standard for classification is the estimated critical value of the income gap

Conclusions

Recent issues on China's uneven regional development of RETI and carbon productivity in an orderly manner trigger us to revisit the theoretical links between green innovations and environmental performance. Based on existing studies testing the effect of RETI on pollution emissions, this paper explores its fundamental mechanism, i.e., RETI's effect on green productivity. In this study, we go a step further by relaxing the assumption underlying the traditional works, i.e., the heterogeneity and

CRediT authorship contribution statement

Zheming Yan:Data curation, Validation, Writing - original draft, Writing - review & editing.Baoling Zou:Data curation, Writing - original draft.Kerui Du:Conceptualization, Methodology, Software.Ke Li:Writing - review & editing.

Acknowledgments

We thank two anonymous reviewers for their helpful comments and suggestions which led to an improved version of this paper. The paper is supported by National Natural Science Foundation of China (Grant nos. 71603148, 71873078, 71773028), and the Humanities and Social Science Research Project of the Ministry of Education of China (Grant no. 18YJC790194).

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