Going “green”: trade specialisation dynamics in the solar photovoltaic sector

Energy Policy 39 (2011) 7275–7283

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Going ‘‘green’’: trade specialisation dynamics in the solar photovoltaic sector Bernardina Algieri n, Antonio Aquino, Marianna Succurro Department of Economics and Statistics, University of Calabria, I-87036 Arcavacata di Rende, Italy

a r t i c l e i n f o

abstract

Article history: Received 27 April 2011 Accepted 19 August 2011 Available online 15 September 2011

The present study aims at providing a comprehensive analysis of trade flows and the domestic value creation of the major solar photovoltaic industry at the world level. Solar technologies convert light and heat from the sun into useful energy. The use of the sun’s energy can not only reduce the consumption of conventional fuels, thus reducing the emission of detrimental greenhouse gases, but it can also enable a gain in enhanced fuel and energy security along with lessening costs. In addition, green technologies and industries can promote economic growth and international competitiveness, and can offer new business and employment opportunities. It becomes, therefore, extremely important to deeply explore the dynamics of the solar photovoltaic sector. Specifically, the present work analyses the main global trends of this sector and sketches the key players on the world market, including producers, installers, and top traders. Based on an analysis of trade flows at the 6-digit level, the international specialisation patterns are investigated, and the role of various market and trade drivers, including subsidies in the uptake of solar technologies, is identified and examined. & 2011 Elsevier Ltd. All rights reserved.

Keywords: Solar photovoltaic Trade specialisation International competitiveness.

1. Introduction Climate change, the possibility of fossil fuel scarcity, and the need to improve the security of energy supply have heightened the need for strongly promoting renewable energies. In particular, the need to reduce import dependency on fossil fuel energy has increased after high price swings registered in many producing countries, due to several factors including unstable geopolitics, natural disasters, and financial speculations. With reference to climate change, when the Kyoto Protocol was signed in 1997, binding targets for reducing greenhouse gas emissions were fixed at the international level (Tsoutsos et al., 2008). Since 1990, the European Union has been pursuing a unilateral greenhouse gas emission reduction target of 20% from 1990 emission levels by 20201 (Boringer et al., 2009). The importance of the subject has induced several governments and experts to provide policy suggestions to lessen global warming (Komor and Bazilian, 2005; Fischer and Newell, 2008; Popp, 2010; Arent et al., 2011; Nakicenovic and Nordhaus, 2011; Noll, 2011). But after 20 years, the problem still exists, while policies implemented to slow down the phenomenon have largely failed. The Climate Summits held in Copenhagen in 2009 and in Cancun in 2010 proved unable to produce a binding international agreement to replace the Kyoto

n

Corresponding author. Tel.: þ39 0984492443. E-mail addresses: [email protected] (B. Algieri), [email protected] (A. Aquino), [email protected] (M. Succurro). 1 In a recent roadmap presented by the European Commission, the target has been increased to 25%. 0301-4215/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2011.08.049

Protocol in the period beyond 2012, when the original commitment period ends (Nakicenovic and Nordhaus, 2011). Given that many countries face problems meeting their greenhouse gas targets, the exploitation of renewable energy technologies through specific incentives and the promotion of ‘‘green’’ trade could certainly contribute to achieve international commitments and facilitate sustainable economic growth (UNCTAD, 2009; UNCTAD, 2011). Barnes and Floor (1996), Hamwey (2005), Steenblik (2005), UNEP (2009), Jha (2009), and Steenblik and Geloso Grosso (2011) suggest that more trade in environmental goods and services would help to disseminate cleaner technologies across countries, especially developing countries, produce a climate of improved policy making and administrative procedures, create new investment opportunities, amplify the variety of climate-friendly products, and reduce prices due to higher international competition. Indeed, the use of the sun’s energy can reduce the consumption of conventional fuels, thus reducing the emission of detrimental greenhouse gases, which account for 41% of emissions worldwide (IEA, 2008). Solar energy can be harnessed with both solar photovoltaics (PV) and solar thermal collectors. Solar PV—both silicon-based and thin film—converts sunlight directly into electricity. Solar thermal collectors capture solar radiation to heat water for homes and swimming pools. Renewable energy sources, therefore, are among the best technology options available to reduce carbon emissions in the electricity sector. The present work focuses its attention on the dynamics of solar photovoltaics, because the sector has significant economic growth and employment potential, as well as considerable trading opportunities. Specifically, the solar PV sector is relatively trade-intensive when

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compared to other renewables, such as the wind industry (Funk Kirkegaard et al., 2010). In addition, the solar PV sector is very liberalised with low tariff levels. According to Jha (2009), the main importers from developing countries apply tariffs at around 8%, while industrialised countries have very low or zero tariffs. Solar photovoltaic energy is thus deemed to become a key energy source in Europe by 2020, fulfilling up to 12% of the continent’s electricity demand (EPIA, 2010). In this context, the study aims to provide a comprehensive analysis of the main global trends in the PV sector, including the key players on the world market and the responsiveness of solar panel exports to income and price competitiveness changes. Based on an analysis of trade flows at the 6-digit level, the international specialisation patterns are investigated, distinguishing between one-way and two-way trade, and the role of various market and trade drivers are identified and examined. The rest of the paper is organised as follows. Section 2 provides an overview of the photovoltaic world market. Section 3 offers a competitiveness analysis through the computation of specific indices, namely the Balassa and Grubel–Lloyd indices, in order to gauge the nature of trade flows. Section 4 presents an econometric analysis of the determinants of US exports in photovoltaic panels using the Johansen procedure on 10-digit data. Section 5 concludes.

Table 1 Annual solar photovoltaic panel production (Megawatts) by country, 1995–2009. Source: http://www.earth-policy.org/data_center/C23. Year

China

Japan

Taiwan

Germany

US

Others

Total

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

n.a. n.a. n.a. n.a. n.a. 3 3 10 13 40 128 342 864 2,013 3,782

16 21 35 49 80 129 171 251 364 602 833 926 938 1,268 1,508

n.a. n.a. n.a. n.a. n.a. n.a. 4 8 17 39 88 170 387 813 1,439

n.a. n.a. n.a. n.a. n.a. 23 24 55 122 193 339 469 744 1,334 1,364

35 39 51 54 61 75 100 121 103 139 153 178 269 401 587

n.a. n.a. n.a. n.a. n.a. 48 70 97 131 186 241 374 545 1,261 2,000

78 89 126 155 201 277 371 542 749 1,199 1,782 2,459 3,746 7,089 10,680

Table 2 Market share of major PV cells producers in 2010. Source: Samsung Economic Research Institute, 2011. Enterprises

Nationality

Production quota in global market (%)

First Solar Sun Tech Q-Cell Yingli Ja Solar

US China Germany China China

20 12 10 9.0 9.0

2. The solar photovoltaic world industry 2.1. Production and installation Green industry is growing rapidly with renewable energy as the central pillar of growth. In particular, the photovoltaic industry is a very dynamic sector with rising growth rates and a good employment perspective (REN21, 2011; Worldwatch Institute, 2011). Explicitly, the growth in solar cell production climbed from an annual expansion of 38 percent in 2006, compared to the previous year, to an annual expansion of 89 percent in 2008 compared to 2007. In 2009, its annual growth rate slowed down but remained strong, registering a 51 percent increase than in 2008 (IEA, 2010). One of the reasons for this positive trend has been the decline in prices of solar modules. Specifically, thanks to a new silicon supply and increased competition from thin-film technologies, PV prices experienced a remarkably steady decrease, realising more than 80% cost reduction on a $/Watt peak basis from 1973 to 2011. Other reasons for the increasing growth rates in the PV sector stem from favourable market conditions, valuable energy policies, supportive public programmes, and specific incentives such as cheap loans, tax breaks, and feed-in-tariffs2 (FIT) granted to the sector (JagerWaldau, 2007). Particularly, feed-in-tariffs are the most implemented form of government subsidies.3 At a country level, the early leaders in production, namely the US, Japan, and Germany, have been overtaken by China, which in 2 According to a FIT scheme, the government agrees to buy back the electricity produced by PV systems at much higher prices than current market prices, for a long period of time (10–20 years). Most major countries have some kind of FIT for the solar sector. The FITs can become expensive for the governments, especially when the number of PV installations becomes very large, and the tariffs are too generous. For this reason many governments intend to change their FIT scheme every year, and in some cases put a cap on the amount of PV capacity qualified for FIT. It is reasonable to reduce FITs when the costs per watt are falling rapidly. On the other hand, the governments will not want to phase out or cut FITs too abruptly, because doing so would kill the solar sector. 3 In most EU member states, prices paid for ‘self-produced’ electricity range from 0.29 to 0.58 h/kWh. For instance, in Germany feed-in-tariffs go from 0.29 to 0.55 h/kWh, in Italy from 0.36 to 0.44 h/kWh, in the Netherlands from 0.46 to 0.58 h/kWh, in Portugal from 0.31 to 0.45, in Spain from 0.32 to 0.34, in Greece feed-in tariffs are set to 0.55 h/kWh, and in UK 0.42 h/kWh (Europe Energy Portal, 2011).

Table 3 Annual solar photovoltaic installations (Megawatts) in selected countries and the world, 1998–2009. Source: Earth Policy Institute, 2011. Year

Germany

Italy

Japan

US

Spain

Others

World

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

10 12 40 78 80 150 600 850 850 1,107 2,002 3,800

n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. 10 70 338 730

69 72 112 135 185 223 272 290 287 210 230 484

n.a. 17 22 29 44 63 90 114 145 207 342 477

0 1 n.a. 2 9 10 6 26 88 560 2,605 69

76 95 94 90 121 148 84 41 223 276 766 1,656

155 197 278 334 439 594 1,052 1,321 1,603 2,430 6,283 7,216

2009 produced more than twice as many solar panels as Japan. Lately, Taiwan’s production has increased fast and may overtake Japan in 2010 (Table 1). At a firm level, a few early movers are dominating photovoltaic global manufacturing production. Specifically, five enterprises hold 60% of the global PV production in 2010; these companies are American First Solar with a production quota of 20%, Chinese Sun Tech, German Q-Cell, and Chinese Yingli and Ja Solar (Table 2). As for the power installed, Europe covers a leading position, with almost 52.6% megawatts of total installed capacity in Germany and 10.12% in Italy in 2009 (Table 3), representing about 63% of the world market. Japan and the US are following behind (EPIA, 2010; REN21, 2011). In terms of cumulative installed capacity (Table 4), Germany, with an installed PV power generating capacity of almost 10,000 MW, is the worldwide leader in installations. Spain is second with 3,400 MW, followed by Japan, the United States, and Italy.

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Table 4 Cumulative solar photovoltaics installations in ten leading countries and the world, 2009. Source: Earth Policy Institute, 2011. Germany

Spain

Japan

US

Italy

South Korea

Czech Republic

Belgium

China

France

India

World

9,779

3,386

2,633

1,650

1,186

520

465

363

305

272

120

22,893

Cumulative installed capacity in megawatts.

35

30

Billions

25

20

15

10

5

0

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Fig. 1. Export dynamics of photosensitive photovoltaic devices, HS 854140. Trade values in US$. Source: Own Elaborations on United Nations, COMTRADE data.

It is worth noting that, China, the world’s largest manufacturer of solar panels, has a low installed capacity (of only 305 MW in 2009). This reveals an interesting feature. Indeed, when comparing production and installation, the fact that Germany installs more than it produces is an indication that the country tends to import solar PV products. Vice-versa, China and Japan record higher production than installation, and thus what remains from production is exported. Since the increase in global installed capacity tends to rise in tandem with international trade in solar PV products, the next two sections will provide a thorough analysis of trade flows.

3. Trade in solar photovoltaic panels 3.1. Data issues and market shares Mapping out trade flows for solar PV raises several data issues. Specifically, export–import patterns can be identified under the 6-digit Harmonised System Code (HS) at an international level, the 8-digit Combined Nomenclature Classification (CN) at the European level and the 10-digit Harmonised Tariff Schedule (HTS) code only works for the US. According to the 6-digit International Trade Harmonised System Code, photovoltaic panels (cells and modules) are a part of the category HS 854140, ‘‘Photosensitive Semiconductor Devices, Photovoltaic Cells and Light-Emitting Diodes.’’ According to the more disaggregated 8-digit CN classification, PV are included into the CN category 85414090, ‘‘Photosensitive Semiconductor Devices, Photovoltaic Cells.’’ According to the HTS code, PV products are identified in the category HS 8541406020 ‘‘Solar Cells Assembled Into Modules Or Panels.’’ The 8-digit Combined Nomenclature, compiled by Eurostat, allows for separation of solar PV goods from light emitting diodes. For our specialisation analysis, we have considered the 6-digit code,

because although it is a broader category, it has the advantage of making data internationally uniform, as the CN code provides data only for the EU countries and the HST code only for the US. In addition, even though the 6-digit code includes unrelated lightemitting diodes, it can be considered as a reasonable indicator of trade in PV panels, because the more disaggregated 8-digit Combined Nomenclature Classification represents more than 90% of the EU import–export under the HS 854140 code (Jha, 2009). Data in the HS 854140 category, extracted from the Commodity Trade Statistics Database (COMTRADE, 2011) compiled by the United Nation Organisation,4 show that worldwide exports and imports of solar panels have recorded an exponential growth over time. The increase has been particularly intense during the first eight years of the new millennium, when total world exports reached their peak in 2008. After that year trade in photovoltaic devices recorded a sharp drop likely due to the global financial crisis (Fig. 1). China is emerging as the largest player in PV exports, with rising export trends until 2008 (Fig. 2). Other major exporters are Germany, Japan and the US. Specifically, in 2009 the US had a 9.29% export market share of the total world market, exceeded by China (41.2%), Japan (17.96%), and Germany (17.5%). Other world suppliers with a market share between 2% and 3% were Spain (2.99%), the Netherlands (2.87%), the UK (2.77%), and Belgium (2.24%) (Table 5). Markets in developed countries have grown exponentially during the last few years in response to the subsidies provided for renewable energy consumption and the huge volume of venture capital investment. China seems to have

4 COMTRADE is the largest depository of international trade data and contains well over 1.7 billion data records for 45 years. All commodity values are converted from national currency into US dollars using exchange rates supplied by the reporter countries, or derived from monthly market rates and volume of trade. Energy commodities are classified according to the Harmonised System (HS).

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7050 6050 5050 4050 3050 2050 1050 50 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

-950 Fig. 2. China’s exports of photosensitive photovoltaic devices. Indices 2001¼ 100. Source: Own Elaborations on United Nations, COMTRADE data.

Table 5 World export market share of photosensitive photovoltaic devices. % Values. Sources: Own calculations on COMTRADE data.

Belgium Canada China France Germany Greece Italy Japan Netherlands Spain UK US

1992

1995

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

0.00 2.19 2.55 0.00 12.54 0.00 0.00 44.44 19.80 0.69 0.00 17.78

0.00 1.89 3.30 2.07 11.58 0.00 0.61 39.68 19.40 0.41 5.44 15.61

1.59 1.66 3.92 1.66 10.93 0.00 0.62 44.57 2.21 1.07 4.67 27.11

1.16 1.99 3.54 1.41 10.41 0.00 0.61 47.63 2.28 1.06 4.17 25.73

1.76 1.35 3.66 1.92 12.85 0.00 0.91 43.34 2.50 1.87 3.44 26.41

1.55 1.21 4.84 1.96 11.43 0.02 0.85 53.06 0.00 2.86 2.35 19.86

1.84 1.20 4.87 1.62 12.11 0.01 0.65 53.53 2.46 2.89 2.52 16.30

1.52 0.65 7.42 1.89 10.89 0.01 0.77 53.29 1.75 3.28 2.49 16.05

2.11 0.59 11.93 1.95 12.42 0.01 0.96 45.51 2.90 2.55 3.64 15.44

3.25 0.56 18.37 1.72 16.45 0.02 0.91 38.82 2.04 1.94 3.80 12.13

2.65 0.50 28.33 1.45 19.00 0.01 0.58 29.51 2.61 1.04 4.00 10.32

2.64 0.54 38.72 1.43 20.82 0.01 0.97 20.41 1.96 1.24 3.46 7.79

2.24 0.33 41.20 1.66 17.54 0.02 1.14 17.96 2.87 2.99 2.77 9.29

particularly benefited from this. In addition, China’s significant share in PV trade is likely linked to the fact that several components of solar technology have now become tradable. The export dynamics from 1994 to 2010 registered in the Euro Area and in the major competitor countries are reported in Figs. 3 and 4. In 2009 the top foreign importers of photovoltaic devices (HS 854140 category) were Germany (35.34% of total), China (15.99%), Italy (10.55%), the US (9.62%), and Belgium (6.12%). The share of Spain in world imports of photosensitive photovoltaic devices dropped from 26.8% in 2008 to 4.5% in 2009, most likely as a consequence of the crisis of the construction sector, which was particularly strong in that country and the relevant cut of government incentives to the photovoltaic sector (Table 6). 3.2. A competitiveness analysis: inter-industry trade specialisation and two-way trade To have a clearer picture of PV solar panel international trade specialisation, inter- and intra-industry indices have been computed. Inter-industry trade refers to the simultaneous exchange of different goods. Countries specialise by exploiting their comparative advantages arising from differences in technology (Ricardo, 1817), innovativeness (Posner, 1961), and relative factor endowments (Heckscher, 1919; Ohlin, 1933). Intra-industry trade is defined as the simultaneous export and import of goods that

belong to the same sector (Vollrath, 1991). Intra-industry trade is prevalent in countries, regions, and industries where increasing return-to-scale in production, monopolistic competition, and ¨ 1996). product differentiation play important roles (Erkkila, To carry out our empirical analysis, we have adopted the Balassa index (1965) for measuring inter-industry specialisation. This index considers comparative advantages as revealed by international trade, so that trade ‘‘reflects relative costs as well as differences in non-price factors.’’ The Balassa index is formally given by xyi Byi ¼ 100  PN

y¼1

xyi

PM 1 xyi = PN i ¼ PM y¼1

i¼1

xyi

ð1Þ

where xyi stands for country i’s exports of commodity y. The Balassa index has a lower bound of zero and no upper bound. A country that is more specialised in an industry than the average of all countries taken together has an index value greater than 100 for that industry, and, conversely, a value below 100 reveals a lack of specialisation compared to the average for all countries. Thus, values above 100 indicate the presence of comparative advantages. The standard deviation of this index across products can be used as a measure of the comparative importance of inter-industry specialisation. The greater the degree of inter-industry specialisation, the greater the standard deviation of the Balassa index. The calculated values for the Balassa index, referring to PV panels, show that China, Japan, Germany, and the US have always exhibited a comparative

B. Algieri et al. / Energy Policy 39 (2011) 7275–7283

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1100

900

700

500

300

100

-100

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 France

Germany

Italy

Netherlands

Spain

Fig. 3. PV Exports of the main Euro Area countries. Indices 2001¼ 100. Sources: Own calculations on COMTRADE data.

700

600

500

400

300

200

100

0

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Canada

Japan

United Kingdom

USA

Fig. 4. PV Exports outside the Euro Area. Indices 2001¼ 100. Sources: Own calculations on COMTRADE, data.

advantage in this product category. In particular, China from 1997 onwards has progressively strengthened its advantages, going from a Balassa index of 104.7 in 1997 to 820.92 in 2009, which means that the country has gained competitiveness in the production and trade of this product group. Japan, Germany, and the US have recorded a wave-like specialisation intensity, meaning that in some years their competitiveness has been strong, while in others it has slowed down. The Netherlands and the UK, with a few exceptions, also show a comparative advantage from 1992 to 2009. Some countries endowed with sunnier climates such as Italy, Greece, and France are despecialised in this sector, while Spain has shown

increasing specialisation since 2004. Canada has recorded a progressive de-specialisation during the same period, i.e., its comparative disadvantage became more pronounced. (Table 7). Intra-industry trade is conventionally measured by the Grubel and Lloyd index (1975) defined as PN 9xij mij 9 GL ¼ 1 PiN¼ 1 ðx i ¼ 1 ij þmij Þ

ð2Þ

where xij and mij are the exports and the imports of commodity j and country i. GL index values can range between zero and one.

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Table 6 World import market share of photosensitive photovoltaic devices. %Values. Sources: Own calculations on COMTRADE data.

Belgium Canada China France Germany Greece Italy Japan Netherlands Spain UK US

1992

1995

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

0.00 4.07 1.26 0.00 19.85 0.10 0.00 14.94 19.16 1.61 0.00 39.01

0.00 2.70 2.39 6.49 14.65 0.03 3.80 11.44 13.47 1.06 10.44 33.52

1.97 4.50 9.26 3.44 15.95 0.09 3.45 10.53 1.72 1.40 8.02 39.67

1.62 4.38 10.01 3.25 14.90 0.03 3.48 11.92 1.28 1.04 9.91 38.18

2.65 4.06 12.11 3.18 20.27 0.05 4.10 10.32 1.45 1.43 6.50 33.88

2.66 3.35 22.57 3.00 21.59 0.05 2.84 12.64 1.29 1.90 3.18 24.93

3.07 2.48 26.03 3.21 18.48 0.09 2.71 14.48 3.16 1.57 3.91 20.81

2.17 2.20 27.37 3.09 24.92 0.08 2.64 13.28 3.18 1.36 3.12 16.59

2.31 2.11 25.03 2.56 31.41 0.04 2.69 11.08 3.54 2.46 3.19 13.57

3.10 1.63 22.68 2.34 30.11 0.05 3.64 9.13 2.32 7.57 3.47 13.97

2.85 1.11 20.87 2.91 26.63 0.07 4.91 6.19 1.70 17.56 3.39 11.80

3.27 0.87 14.32 3.32 27.02 0.22 6.27 4.57 1.53 26.76 2.92 8.94

6.12 1.00 15.99 5.91 35.34 0.49 10.55 4.50 3.65 4.53 2.30 9.62

Table 7 The Balassa index of comparative advantages in photovoltaic solar panels. Sources: Own calculations on COMTRADE data. RCA

1992

1994

1997

2000

2004

2007

2008

2009

Canada China France Germany Greece Italy Japan Netherlands Spain UK US

64.19 118.03 n.a 114.59 0.06 n.a 514.53 556.46 42.07 n.a 156.31

67.27 154.74 72.47 145.33 0.13 15.94 616.02 712.65 41.74 169.00 201.35

39.30 104.70 44.64 130.23 0.05 15.96 555.01 101.61 37.88 90.44 213.77

59.80 117.99 39.70 157.36 0.25 21.10 825.70 88.71 77.90 122.45 274.00

64.34 391.07 142.66 373.71 18.84 67.96 2946.87 171.80 561.67 223.34 613.81

34.96 678.47 78.39 417.75 9.98 33.98 1207.34 159.38 120.26 265.58 259.47

32.09 737.84 65.41 387.06 10.18 48.76 711.94 98.12 121.31 206.32 163.46

25.19 820.92 85.76 372.32 22.03 67.24 740.46 159.30 320.74 188.86 210.46

A zero value implies a complete inter-industry trade of country i in commodity j, while a value of one stands for complete intra-industry trade. A series of low GL values of one region or country reflect a centripetal process of industrial agglomeration and high specialisation, while a series of high GL values reflect a centrifugal process of industrial dispersion. This index is unbiased if, and only if, total trade of country i is balanced, otherwise, the index is a downward-biased summary measure of intra-industry trade for each commodity. One way of correcting such downward bias towards intra-industry trade is to estimate the values of exports and imports if trade was balanced and calculate the index with these new values. A weighted average of the values of the new index gives the corrected summary measure of the proportion of intra-industry trade in i’s total trade. The calculated Grubel–Lloyd index for our sample allows us to better understand the nature of export–import patterns in solar PV, namely if trade flows are more bidirectional or unidirectional. Explicitly, the results (Table 8) suggest that the US, the UK, the Netherlands, and Germany simultaneously export and import photovoltaic panels, that is, trade has more of an intra-trade nature and these countries have a relatively balanced solar PV trade. A high GL index could be considered, on one hand, as an indicator of a higher degree of liberalisation and integration of the economy in the international market, or on the other hand, as an indicator of the phenomenon of fragmentation in production processes that makes firms from various countries produce additional flows within the total trade. The trade of Greece, Italy, and Japan has more of an inter-industry nature, that is, their trade flows are unidirectional. In particular, when considering the Balassa index, it becomes clear that Greece and Italy import more than they export, and therefore that these countries run a substantial trade deficit in solar PV goods. Conversely, Japan enjoys a trade surplus. The trade of China and France has no distinct inter or intra-industry nature. Belgium and Canada turned the nature of their trade from an intra-industry trade

Table 8 Grubel–Lloyd index in photovoltaic solar panels. Sources: own calculations on COMTRADE data.

Belgium Canada China France Germany Greece Italy Japan Netherlands Spain UK US WORLD

2008

2009

0.886 0.756 0.547 0.594 0.861 0.082 0.264 0.372 0.883 0.087 0.924 0.923 0.748

0.522 0.484 0.573 0.427 0.648 0.071 0.189 0.412 0.863 0.778 0.924 0.965 0.679

pattern in 2008, into more of an inter-industry oriented trade pattern in 2009, that is their almost balanced export–import flows in 2008 became more import skewed in the following year.

4. PV export determinants 4.1. Price and income elasticities To complete the trade analysis we have empirically investigated the photovoltaic export-drivers, by estimating a classical export equation for the US over the period 1991:12–2009:12. The analysis focuses on the US for the greater availability of time series data. In particular, the photovoltaic export equation is

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expressed as follows: ln expt ¼ f þ

a b

1 ln wt  ln pt þ xt

b

7281

Table 9 Johansen cointegration test. Sample 1991:12–2009:12.

ð3Þ

where the dependant variable expt is the exports of photovoltaic panels in value terms; the chosen explanatory variables include income and price variables. Explicitly, w is the income variable, p is the relative export price. a/b and 1/b are the coefficients that need to be estimated empirically, x is the disturbance term independently and identically distributed (i.i.d.) and subscript t stands for time. With regard to the dataset, PV exports (expt) in dollars at constant prices (2000¼100) were collected from COMTRADE. In detail, the country-specific US HTS 10-digit trade data have been considered. This classification allows for a finer identification of solar PV exports, as it is equivalent to internationally uniform HS code, down to the 6-digit level, but significantly more detailed. Given the high correlation between GDP growth and industrial production (Paribas, 2008; Eurostat, 2007), the income variable (wt) is expressed in terms of world industrial production (2000¼100), this proxy avoids de facto to transform original data. Monthly data for GDP or disposable income are, in fact, not available, therefore one should convert quarterly data of GDP into monthly frequencies. The adoption of the world industrial production as a measure of income is also in line with the work by Aurangzeb et al. (2005). Income figures have been taken from IMF via DataStream. PV prices have been collected from Berkeley Lab’s Environmental Energy Technologies Division. An upsurge in the relative price implies a drop in US’s competitiveness and a contraction in PV exports. For all variables, monthly data seasonally adjusted5 have been considered. 4.2. Johansen cointegration analysis The quantitative variables were first transformed into natural logarithms, and then the stationary property of the series was analysed using the Augmented Dickey and Fuller (ADF) test (1979). The results,6 summarised in Table A1 (Appendix), indicate that all variables are integrated of order one, i.e., the series becomes stationary after its first differentiation. The next step was to apply the cointegration procedure as developed by Johansen7 in order to test the presence of long-run equilibrium relationships among variables in Eq. (3). The results of Johansen’s test for cointegration8 are reported in Table 9. The table shows the trace and the max eigenvalue statistics. The trace statistic of r cointegration relations is a sequence of P likelihood ratio tests, computed as ltrace ðrÞ ¼  T ki ¼ r þ 1 logð1lbi Þ where li is the estimated value of the characteristic roots (also called eigenvalue) obtained from the estimated long-run P matrix9 and T is the number of usable observations. 5 The seasonal nature of the variables was adjusted using the Census X-12 procedure. 6 The lag length for each variable has been selected on the basis of the Schwarz Bayesian criterion, which chooses the appropriate lag length by trading off parsimony against reduction in the sum of the square. 7 To identify the appropriate model, the five possibilities considered by Johansen (1995) were tested, specifically: (1) the series have no deterministic trends and the cointegrating equations do not have intercepts, (2) the series have no deterministic trends and the cointegrating equations have intercepts, (3) the series have linear trends but the cointegrating equations have only intercepts, (4) both series and the cointegrating equations have linear trends, and (5) the series have quadratic trends and the cointegrating equations have linear trends. In our case, a constant and a linear trend best describe the data; hence, the fourth specification was selected. Note that the trend variable acts as a proxy for missing variables outside the information set, such as consumer preferences and subsidies. 8 The lag length was chosen on the basis of the Aikaike Information criterion (Judge et al., 1988), namely, a 2-lag structure minimises the information criteria; therefore, this length was considered. 9 For a detailed description of the testing procedure, see Johansen (1995).

H0

H1

Eigenvalue Statistic

Null Hypothesis

Alternative

r¼0 r r1 r r2

r 40 r 41 r 42

Critical Value Prob.nn

Trace

0.05

0.127324 0.039712 0.030023

44.33982 15.19507 6.523314

42.91525 25.87211 12.51798

0.0357 0.5583 0.3969

Null Hypothesis Alternative 0.127324 r¼0 r ¼ 1n r¼1 r¼2 0.039712 r¼2 r¼3 0.030023

Max-Eigen 29.14475 8.671756 6.523314

0.05 25.82321 19.38704 12.51798

0.0176 0.7577 0.3969

n

r indicates the number of cointegrating vectors. n

denotes rejection of the hypothesis at the 0.05 level. MacKinnon et al. (1999) p-values.

nn

The first row of the trace statistic tests the hypothesis of no cointegration, the second row tests the hypothesis of at most one cointegrating relation, the third row tests the hypothesis of at most two cointegrating relations, all against the alternative of k cointegrating relations, where k is the number of endogenous variables, for r¼0, 1, y, k 1. Since the value 44.33 exceeds the 5% critical value of the ltrace statistic, the null hypotheses of non-cointegration can be rejected. Therefore, ltrace indicates that there is at most one cointegrating vector at the 5% significance level. To verify the previous findings, the lmax eigenvalue statistics have been computed. The latter is calculated as lmax ¼ Tlogð1ld r þ 1 Þ. The obtained results confirm the trace test findings: the null hypothesis of no cointegrating vector (r¼0) can be rejected at the 5% level, while the null of r¼1 cannot be rejected at all. lmax thus shows that there is one cointegrating equation at the 5% significance level. The estimation of the VEC model is carried out in two stages. In the first stage, the normalised cointegrating relation from the Johansen procedure, which expresses the long-run equilibrium state, is estimated. Specifically, the long-run equation is expressed as lnexpt ¼ 7:64þ 2:69ln wt 1:15ln pt þ 0:011trend ð6:50Þ

ð3:18Þ

ð5:04Þ

2

R ¼ 0:74; S:E: equation ¼ 0:005145; Fstatistic ¼ 165:59 Akaike AIC ¼ 7:66 Log likelihood ¼ 828:13

ð4Þ

with absolute asymptotic t-ratios in brackets. Eq. (4) includes a constant and a deterministic trend, which could mirror several phenomena such as public programmes, consumer’s preferences, and subsidies. Then the error correction term is computed from the estimated cointegrating relation and a VAR in first differences including the error correction term as regressor is estimated. In particular, the coefficients on the lagged first difference terms are the short-run parameters, while the coefficient D(ln exp) measures the speed of adjustment towards the equilibrium (Table 10). The results are satisfactory: the long-run coefficients of relative price and foreign income are significant and have the expected signs. An upsurge in the relative PV price causes a drop in PV exports, whereas a rise in world income leads to higher PV export revenues. Since the estimated elasticities exceed unity, the demand for PV export is income and price elastic. A 1% increase in price reduces PV exports by 1.15%, while a 1% increase in income raises exports by 2.69%. The coefficient of the relative price should be interpreted as an indicator of competitiveness, such that if strong competition is overcome, the pay-off could be significant. The price variable has a lower elasticity than the income variable, meaning that foreign income is the major factor that drives solar panel exports. Furthermore, since income elasticity exceeds unity, solar panels are regarded as ‘‘superior’’ goods, and thus an

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Table 10 Adjustment coefficients. D (ln exp)

 0.003294 ( 1.98)

D (ln p)

0.002631 (  1.92)

D (ln w)

0.069455 (5.09)

Short-Run VECM D(ln exp ( 1)) D(ln exp ( 2)) D(ln p (  1)) D(ln p (  2)) D(ln w ( 1)) D(ln w ( 2))

0.919719 (13.2691)  0.019851 ( 0.28915)  0.166310 ( 2.13868)  0.203036 ( 2.22450) 0.01871 (2.6183) 0.372611 (5.9225)

increase in income is expected to raise substantially the demand for exports. This could be explained by the fact that when income rises, people become more and more concerned about the environmental effects of energy production. The trend variable is significant, but its impact is less pronounced than the other variables. This means that other factors, such as consumers’ preferences and public incentives, play a minor role in pushing exports. The error correction term has the corrected sign and the speed of adjustment coefficient indicates a slow correction of actual exports to its long-run equilibrium. The model has been validated using a variety of residual diagnostics. Table A3 (Appendix) reports the Lagrange-multiplier (LM) test for serial correlation in the disturbances up to the order of 12. The test statistic for lag order h is computed by running an auxiliary regression of the residuals xt on the original right-hand regressors and the lagged residual xt  h (Johansen, 1995). Under the null hypothesis of no serial correlation of order h, the LM statistic is asymptotically distributed w2 with k2 degrees of freedom. The probability values indicate that the null hypothesis of no serial correlation is not rejected; therefore, there are no serial correlation problems. To verify the normality of the residuals, the multivariate extension of the Jarque–Bera residual normality test was computed. Specifically, this compares the third and fourth moments of the residuals to those from the normal distribution (Table A4, Appendix). Under the null hypothesis of a normal distribution, the Jarque–Bera statistic is distributed as w2, with six degrees of freedom. With a joint probability equal to 0.59, the null is not rejected. The residuals pass also the heteroskedasticity test and the regression equation turns out to be stable, given that all roots have moduli of less than one and lie inside the unit circle.

5. Conclusion In recent years green technologies and industries have gained considerable attention because they can be considered as an engine of a new development paradigm. In particular, renewable energy sources can offer energy independence (above all from oil and fossil fuels), can reduce sensibly the rising concentration of CO2 and thus pollution, and can prevent climate change and global warming. The present study has presented a picture of the photovoltaic sector at both the global and disaggregated level. The analysis has shown that the production and installation of photovoltaic technologies have increased over time. The trade analysis has revealed that China is emerging as the largest player in PV world market, with a substantial share of global trade. China’s high share in trade in PV is also

indicative of the fact that several components of solar technology have now become tradable. The analysis of trade flows has been carried out with the Balassa and the Grubel–Lloyd indices. In particular, the Balassa index shows that China, Germany, Japan, and the US have a comparative advantage in solar panels. For China, this advantage has strengthened over time, meaning a strong gain in international competitiveness. The Netherlands and the UK, with some exceptions also show a comparative advantage in the period from 1992 to 2010; while surprisingly, countries endowed with more sunshine, such as Italy, Greece, and France are despecialised in this sector and run considerably deficit in their trade balance for this product category. Spain enjoys an increasing specialisation and competitiveness since 2004. The calculated Grubel Lloyd index for our sample suggests that the US, the UK, the Netherlands, and Germany simultaneously export and import photovoltaic panels, that is, their trade has more of an intra-industry nature. This can be an indication of a higher degree of integration of these economies in the world market, and a signal of a stronger fragmentation in the production processes at the global level. Trade of Greece and Italy has mainly an inter-industry nature, while China and France have no distinct inter or intra-industry trade nature. Finally, the estimation of the determinants of PV exports for the US indicates the presence of a long-run equilibrium relationship between PV exports, foreign income, and relative prices and shows that the price and income elasticities of demand for photovoltaic panel exports are all above unity.

Appendix A See Tables A1–A4. Table A1 Unit root tests (Augmented Dickey–Fuller). ADF

ln exp ln w ln p

Levels

First Differences

 1.85  2.16  1.46

 3.05  5.24  2.95

Note: MacKinnon critical values ( 3.46 at 1% level and  2.87 at 5% level). Null hypothesis of unit root. Table A2 LM test statistics for residual serial correlation. Sample: 1991:12, 2009:12—Null hypothesis: no serial correlation at lag order h. Lags

LM-Stat.

Prob.

1 2 5 6 9 10 12

9.568257 12.14330 6.284136 10.57720 7.671325 8.743360 7.066710

0.3866 0.2054 0.7112 0.3058 0.5676 0.4613 0.6302

Note: Probs from chi-square with 9 df.

Table A3 ¨ Residual Normality Test Cholesky (based on Lutkepohl, 1991). Component

Jarque–Bera

df

Prob.

Joint

3.558

6

0.5962

Note: Null hypothesis: residuals are multivariate normal.

B. Algieri et al. / Energy Policy 39 (2011) 7275–7283

Table A4 VECM residual heteroskedasticity tests. Joint test: Chi-sq

df

Prob.

215.8711

216

0.4897

Note: Null hypothesis: residuals are homoskedastic.

References Arent, D.J., Alison Wise, A., Gelman, R., 2011. The status and prospects of renewable energy for combating global warming. Energy Economics. doi:10.1016/ j.eneco.2010.11.003. Aurangzeb, A., Stengos, T., Mohammad, A.U., 2005. Short-run and long-run effects of exchange rate volatility on the volume of exports: a case study for pakistan. International Journal of Business and Economics 4 (3), 209–222. Balassa, B., 1965. Trade liberalisation and revealed comparative advantage. The Manchester School of Economics and Social Sciences, 33. Barnes, D.F., Floor, W.M., 1996. Rural energy in developing countries: a challenge for economic development. Annual Review of Energy and the Environment 21, 497–530. ¨ Boringer, C., Loschel, A., Moslener, U., Rutherford, T.F., 2009. EU climate policy up to 2020: an economic impact assessment. Energy Economics 31, S295–S305. BPN Paribas (2008), Eurozone: Industrial production contracted again, February 08 – 061. COMTRADE (2011), Database /http://comtrade.un.org/S, United Nations. Dickey, D.A., Fuller, W.A., 1979. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association 74 (366), 427–431. Earth Policy Institute (2011), Solar Energy Data from World on the Edge, January 12, /http://www.earth-policy.org/data_center/C23S. EPIA (2010), Electricity From The Sun, available at /www.epia.orgS. ¨ M. (1996), ‘‘Finnish Participation in the Internal Market,’’ in Alho, K. et al. Erkkila, (Eds.), The Economics and Policies of Integration – A Finnish Perspective, ETLA series A22. Europe Energy Portal (2011), Feed-in tariffs, /http://www.energy.eu/#FeedinS. Eurostat (2007), European business, Facts and figures. Fischer, C., Newell, R., 2008. Environmental and technology policies for climate mitigation. Environmental Economics and Management 55 (2), 142–162. Funk Kirkegaard J., Hanemann T., Weischer L., and Miller M. (2010), Toward a Sunny Future? Global Integration in the Solar PV Industry, Policypointers WP 10/6 /http://www.policypointers.org/Page/View/11201S. Grubel, H.G., Lloyd, P.J., 1975. Intra-industry Trade. The Macmillian Press. Heckscher, E., 1919. The Effect of Foreign Trade on the Distribution of Income. Ekonomisk Tidskrift, 497–512. Hamwey R. (2005), Environmental goods: where do dynamic trade opportunities for developing countries lie? WP, Centre for Economic and Ecological Studies, Geneva. IEA (2010), World Energy Outlook 2010. Paris: International Energy Agency. IEA (2008), CO2 Emissions from Fuel Combustion. OECD/IEA, Paris.

7283

Jager-Waldau, A., 2007. Photovoltaics and renewable energies in Europe. Renewable and Sustainable Energy Reviews 11, 1414–1437. Jha V. (2009), Trade flows, barriers and market drivers in renewable energy supply goods, ICTSD, Issue Paper 10, Geneva, Switzerland. Johansen, S., 1995. Likelihood-Based Inference in Cointegrated Vector Autoregressive Models. Oxford University Press, Oxford. Judge, G.G., Hill, R.C., Griffiths, W.E., Lutkepohl, H., Lee, T.C., 1988. Introduction to the Theory and Practice of Econometrics, second ed. John Wiley, New York. Komor, P., Bazilian, M., 2005. Renewable energy policy goals, programs, and technologies. Energy Policy 33 (14), 1873–1881. ¨ Lutkepohl, H., 1991. Introduction to Multiple Time Series Analysis. Springer Verlag, Berlin. MacKinnon, J.G., Haug, A., Michelis, L., 1999. Numerical Distribution Functions of Likelihood Ratio Tests for Cointegration. Journal of Applied Econometrics 14 (1), 563–577. Nakicenovic, N., Nordhaus, W., 2011. Editors’ introduction: the economics of technologies to combat global warming. Energy Economics. doi:10.1016/ j.eneco.2011.02.001. Noll, R.G., 2011. Encouraging green energy r&d: comment. Energy Economics. doi:10.1016/j.eneco.2011.02.002. Ohlin, B., 1933. Interregional and International Trade. Harvard University Press, Cambridge, MA. Popp, D. (2010), Innovation and Climate Policy, National Bureau of Economic Research, Working Paper 15673. Posner, M.V. (1961), International Trade and Technical Change Oxford Economic Papers, vol. 13, pp. 323–341. REN21 (2011), Renewables 2011, Global Status Report, Renewables Energy Policy Network. /http://www.ren21.net/Portals/97/documents/GSR/REN21_GSR2011. pdfS. Ricardo, D. (1817), Principles of Political Economy and Taxation, Prometheus Books, 59 John Glenn Drive, Amherst, New York, 1996, /www.econlib.org/ library/Ricardo/ricPtoc.htmlS. Samsung Economic Research Institute (2011), Strategies for cultivating green-tech start-ups, /http://apecsmeic.org/data/20/APEC_Green_Innovation_(Kim,_SangTae,_SMBA).pdfS. Steenblik, R. (2005), Liberalisation of Trade in Renewable-Energy Products and Associated Goods: Charcoal, Solar Photovoltaic Systems, and Wind Pumps and Turbines, OECD Working Paper No. 7. Steenblik, R. and Geloso Grosso M. (2011), Trade in Services Related to Climate Change, OECD Working Paper No. 3. Tsoutsos, T., Papadopoulou, E., Katsiri, A., Papadopoulos, A.M., 2008. Supporting schemes for renewable energy sources and their impact on reducing the emissions of greenhouse gases in Greece. Renewable and Sustainable Energy Reviews 12, 1767–1788. UNCTAD (2011), WTO Negotiations on Environmental Goods: selected technical issues. New York and Geneva. UNCTAD (2009), Promoting poles of clean growth to foster the transition to a more sustainable economy, Trade and Environment Review 2009/2010, New York and Geneva, ISSN 1810-5432. UNEP (2009), Climate and Trade Policies in a Post-2012 World, United Nations Environment Programme. Vollrath, T.L., 1991. A theoretical evaluation of alternative trade intensity measures of revealed comparative advantage. Weltwirtschaftliches Archiv 130 (2), 265–279. Worldwatch Institute (2011), Jobs in Renewable Energy Expanding, /http://www. worldwatch.org/node/5821S.