Chinese Renewable Energy Technology Exports: The Role of Policy, Innovation and Markets

Abstract

Chinese companies have become major technology producers, with the largest shares of their output exported. This paper examines the development of solar PV and wind energy technology component (WETC) exports from China and the competitive position of the country‘s renewable energy industry. We also describe the government’s renewable energy policy and its success in renewable electricity generation as well as increasing renewable energy innovation and foreign knowledge accumulation, which may drive export performance. We aim at empirically identifying determinants of Chinese solar PV and WETC exports. We estimate a gravity trade model using maximum likelihood estimation. Besides controlling for standard variables, we consider additional explanatory factors by accounting for market, policy and innovation effects steaming from both importing countries and China. We use a panel dataset representing bilateral trade flows of 43 developed and developing countries that imported solar PV and WETCs from China between 1996 and 2008. Empirical results indicate that high income countries, with large renewable energy markets and demand side policy support schemes, in terms of incentive tariffs, are increasingly importing Chinese solar PV components. We show that trade costs have a negative impact on exports of solar PV components but not WETC. Additionally, we find a positive impact of research and development appropriation growth, especially from provincial governments in China, but no evidence that bilateral knowledge transfer and indigenous innovation affect exports.

Appendices

Appendix 1

See Tables 7, 8, 9 and 10.

Table 7 Nomenclature of solar photovoltaic and wind energy components in, HS 1996

Table 8 Development of Chinese exports and imports of components for potential renewable energy use and export and import market shares compared to selected countries

Table 9 Trade related sector specific indicators of competitiveness of China and selected countries by components for potential renewable energy use

Table 10 Share of region and selected countries on Chinese exports and imports of potential renewable energy components (in %)

Appendix 2

See Tables 11, 12, 13 and  14

1.1 Patents as innovation and technology transfer indicator— methodology

Innovation can be measured using different indicators such as R&D expenditure, number of researcher (input measure of the inventive process) and patents (output measure of inventive process). Different means of technology transfer are identified and used in the literature. The most common are licensing, FDI, international trade and movement of personnel. Yet, cross country data on most of these indicators when analyzing a specific sector is not available. Several researchers lined out that patent data, can be preferable as it provides disaggregated information on the innovation in terms of technology, inventor and applications process (Griliches 1990; Jaffe 1986). This detailed information allows studying sector specific technology transfer and knowledge spillovers across countries (Dechezleprêtre et al. 2010).

Table 11 Description for solar PV and wind energy technology relevant IPCs

Table 12 Correlation matrix for analysis of solar photovoltaic energy component imports

Table 13 Correlation matrix for analysis of wind energy component imports

Table 14 Descriptive statistics

Consequently, this study adopts the approach by Dechezleprêtre and Glachant (2012) and Dechezleprêtre et al. (2011) to account for innovation and technology transfer in PV and wind energy using the classifications scheme developed by Johnstone et al. (2010) and provided by the WIPO IPC Green Inventory (2012) (Table 11).

National innovation is measures as the number of patents invented and filed for in country $i\, (InnoPatApp_{i t} )$ . The number of innovations transferred from country $i$ to $j$ is measured through patents invented in country i and filed in j $(TransfPat_{i j t} )$ . To avoid duplications we use fractional counts of DOCDB patent families. In order to obtain the total number of innovations transferred from all i to j in a sector at time t $(\hbox {TotTransfPat}_{\mathrm{j t}} )$ we use the sum of individual country transfers:

$$\begin{aligned} \hbox {TotTransfPat}_{\hbox {j t}} =\mathop \sum \limits _{\mathrm{j}=1}^\mathrm{i} \hbox {TransfPat}_\mathrm{i j t} \quad \quad \quad i\ne \hbox {j} \end{aligned}$$

(6)

The literature on innovation indicates that the productivity of knowledge strongly depends on the existing knowledge (Porter and Stern 2000; Bosetti et al. 2008). As the process until an innovation reaches the market might take several years it assumed that not only innovation today but also the stock of existing knowledge is relevant to determine its effect on production and export. In order to account for this lag we also use the perpetual inventory method to annual patent applications in wind energy and PV technology. Consequently, the stock of innovation and the stock of innovation, bilateral foreign knowledge stock and total foreign knowledge stock variables are calculated as follows:

$$\begin{aligned} KInno_\mathrm{i t} =\left( {1-\delta } \right) KInno_\mathrm{i t-1} +InnoPatApp_{i t}\end{aligned}$$

(7)

$$\begin{aligned} KTransf_\mathrm{ij t} =\left( {1-\delta } \right) KTransf_\mathrm{ij t-1} +TransfPat_{ij t}\end{aligned}$$

(8)

$$\begin{aligned} KSumTransf_\mathrm{j t} =\mathop \sum \limits _{j=1}^i KTransf_\mathrm{ij t} \end{aligned}$$

(9)

and:

$$\begin{aligned} i\ne j; \delta =0.15 (0.10) \end{aligned}$$

Hence, the stock of innovation (KInno) and transfer (KTransf) equals the respective stock at time t-1 minus its depreciation $(\delta )$ plus patent applications by residents of i in i or plus applications of residents of country i in filed in country j, respectively.¹³