Determinants of Technology Transfer through CDM: the Case of China. by Matthias Weitzel, Wan-Hsin Liu, Andrea Vaona

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1 Determnants of Technology Transfer through CDM: the Case of Chna by Matthas Wetzel, Wan-Hsn Lu, Andrea Vaona No December 2013

2 Kel Insttute for the World Economy, Hndenburgufer 66, Kel, Germany Kel Workng Paper No December 2013 Determnants of Technology Transfer through CDM: the Case of Chna* Matthas Wetzel, Wan-Hsn Lu, and Andrea Vaona Abstract: Technology transfer (TT) s not mandatory for Clean Development Mechansm (CDM) projects, yet proponents of CDM argue that TT n CDM can brng new technologes to developng countres and thus not only reduce emssons but also foster development. We revew the quanttatve lterature on determnants of TT n CDM and estmate determnants for CDM projects n Chna. Chna s by far the largest host country of CDM projects and t s therefore crucal to understand the factors that drve TT there. We focus on heterogenety wthn a sngle country and results can thus be lnked to specfc polces of the country for better nterpretaton. Our probt estmatons confrm results of nternatonal cross-country studes, ndcatng that larger projects and more advanced technologes are more lkely to nvolve TT. In addton, we fnd evdence that agglomeraton effects are more pronounced on the provnce level rather than larger regons. We also fnd a postve effect of FDI on TT and a complementary role of academc R&D engagement to TT. Keywords: Clean Development Mechansm, Technology Transfer, R&D, Agglomeraton, Chna. JEL classfcaton: O33, Q55, Q58 Matthas Wetzel Kel Insttute for the World Economy Hndenburgufer Kel, Germany +49(0) Emal: Matthas.wetzel@fw-kel.de Wan-Hsn Lu Kel Insttute for the World Economy Hndenburgufer Kel, Germany +49(0) Emal: wan-hsn.lu@fw-kel.de Andrea Vaona Unversty of Verona, Va dell'artglere Verona, Italy andrea.vaona@unvr.t. * We would lke to thank partcpants of the semnar n Envronmental, Resource and Development Economcs at the Unversty of Kel for helpful comments and Laura Magazzn for helpful dscussons. The usual dsclamer apples. The responsblty for the contents of the workng papers rests wth the author, not the Insttute. Snce workng papers are of a prelmnary nature, t may be useful to contact the author of a partcular workng paper about results or caveats before referrng to, or quotng, a paper. Any comments on workng papers should be sent drectly to the author. Coverphoto: un_com on photocase.com

3 1 Introducton The man am of the Clean Development Mechansm (CDM), ntroduced by the Kyoto protocol, s decreasng CO 2 emssons n developng countres, at the same tme gvng buyers n developed countres more flexblty n achevng reducton requrements. Although there s no explct objectve of transferrng knowledge, technology and equpment to developng countres, t s hoped that CDM can brng them advanced low carbon technologes, by connectng partners from developng and developed countres. In addton, revenues from sellng carbon credts mght fnance more advanced technologes that would not have been vable wthout ths revenue stream. CDM thus reduces barrers especally wth respect to fnance costly foregn technology (Schneder et al., 2008). Whle technology transfer (TT) n CDM projects s not well defned n nternatonal legal documents (Das, 2011), there has emerged a lterature tryng to fnd determnants of ths phenomenon (see Secton 2). Prevous studes are manly based on nternatonal cross-sectonal datasets,.e., they try to explan the heterogenety between countres, thus potentally neglectng heterogenety wthn countres. The CDM has led to 7127 regstered projects (as of July 30, 2013). These projects have led to the ssuance of 1353 mllon certfed emsson reductons (CER),.e., they have avoded the equvalent of 1353 Mt of CO 2 emssons. The projects are however dstrbuted very unequally, wth Chna and Inda hostng 52% and 19% of all regstered CDM projects, respectvely. Cross-country estmates are therefore to a large extent drven by these two countres, whose specfctes are however customarly controlled by smple dummes though. As a consequence there s generally no n-depth nterpretaton of these country specfc effects. Provdng ths nterpretaton for Chna s all the more mportant gven that the Chnese government has adopted natonal legslaton to facltate TT n CDM projects. 1 It s stated there that, CDM project actvtes should promote the transfer of envronmentally sound technology to Chna (Artcle 10). Ths, however, does not mean that TT s a necessary condton for project approval and there are no drect ncentves for TT. At the same tme, other restrctons potentally harm the wllngness of foregn frms towards TT. For example, foregn partners are elgble to conduct CDM projects n Chna, however the project owner can only be a Chnese (majorty) owned entty (Artcle 11). Other legslaton requres certan shares of local content n renewable energy technologes. Schroeder (2010) therefore concludes that ntal hopes of Chnese project owners for TT were not entrely met. As far as Chna s concerned, t s very dverse and CDM projects are carred out both n rather developed coastal regons and western regons, the latter have a per capta GDP only half that of coastal provnces. Yet, most exstng nternatonal studes often do not take nto account ths regonal heterogenety. Ths paper tres to fll ths gap, takng a closer look at Chna. On the footsteps of crosscountry studes, we am at nvestgatng whether the determnng factors they hghlghted - such as dfferent levels of development of technologcal capacty - also hold at a regonal level. Ths extends recent studes on TT n Chnese CDM projects whch ether had a dfferent focus or relatvely few controls. In partcular, we measure the agglomeraton effects of exstng CDM projects along dfferent spatal scales and ntroduce a more detaled analyss on the role of R&D expendtures

4 To sum up, our paper provdes a more detaled vew on the drvers of TT through CDM n Chna, whch hosts more than half of all CDM projects worldwde. We do not only nvestgate whether the determnants found to be mportant n an nternatonal context also hold for Chna at a sub-natonal level. Ths paper addtonally analyzes agglomeraton effects on TT through co-locatng wth smlar CDM projects usng dfferent geographc contexts and explores the role of R&D from dfferent nnovators on TT. In ths way, our paper both complements exstng nternatonal studes and t also extends recent papers concernng Chnese projects (Luo and Ye, 2011; Marcon and Sanna-Randacco, 2012; Zheng and Zhang, 2012). The remander of the paper s structured as follows. Secton 2 provdes an overvew of the relevant lterature. Secton 3 descrbes the data and estmaton strategy. Secton 4 dscusses our econometrc results. Secton 5 concludes. 2 Relaton to the exstng lterature There exst several quanttatve studes on TT of CDM projects. In prncple, all studes made use of nformaton publshed n the offcal project desgn document (PDD) descrbng the project. Ths method s not perfect because the nformaton on TT n PDDs s not standardzed. However, followup surveys ndcate that PDDs predct TT suffcently well (Murphy et al., forthcomng). Crtera to measure TT dffered slghtly between studes. Most studes searched the PDDs for ndcatons of nvolvement of foregn partners or supplers. Das (2011) however dfferentated between smple technology mports and superor TT, the latter one specfcally adjustng a technology for the host country or the CDM project. Schmd (2012) analyzed dfferent forms of TT by dfferentatng between transfer n equpment, knowledge, or both. Econometrc (ether probt or logt) estmaton was usually carred out to nvestgate the relevance of dfferent factors n fosterng TT of CDM projects. Most studes regressed the observed flow of TT on project and host country specfc characterstcs. Whle the former ncluded truly project specfc characterstcs (such as the project s abatement potental, ts technology, and startng year), the latter gave nformaton on characterstcs of the host country. Earler studes smply used host country dummes, whle many later studes replaced these host country fxed effects by varables dentcal to all or several projects n a gven host country (e.g. GDP or tarff rates). Some studes (UNFCCC, 2010; Hates et al., 2012) used country fxed effects n a frst stage estmaton and subsequently added a second estmaton whch regressed country characterstcs on predcted TT probabltes from the frst stage (ncludng country dummes). Table 1 presents determnants found to be sgnfcant n prevous studes. In general, studes found that larger projects are more lkely to nvolve TT, whereas ether small-scale ones or those wthout a foregn partner ( unlateral ) are less lkely to nvolve TT. As a consequence, projects whch ether requre more captal or are more complex due to ther sze are more lkely to nvolve TT. 3

5 Table 1: Overvew of determnants of TT n CDM projects n the quanttatve lterature Study Observatons a Determnants of TT b Further controls Remarks + - De Connk et al. (2007) 63 (reg) No regresson analyss Technology types, estmaton of captal flows and nvestment costs Hates et al. (2006) 854 (all) Chna dummy, Sze Unlateral c Technology and country dummes Dechezlepretre et al (2008) Dechezlepretre et al (2009) 644 (reg) Sze, GDP growth, Project frm s subsdary of frm from Annex I country d, Trade, (Technologcal capablty) 644 (reg) Sze, Project frm s subsdary of frm from Annex I country, Trade, GDP growth, Tech. capablty Unlateral, Smlar e, (FDI nflow) Unlateral, Smlar, FDI nflow GDP per capta, Populaton, Technology and country dummes GDP per capta, Populaton, Carbon ntensty, Technology and country dummes Technology and country dummes Technology, country, and year effects. 2 nd stage: per capta GDP, FDI, Fxed captal formaton, Imports Technologcal capablty weakly postve, nteracts postvely for energy/chemcals projects and negatvely wth agrcultural projects Chna dummy negatve, TT n Chna drven manly by hgh growth and technologcal capablty Seres et al. (2009) 3296 (all) Sze Unlateral, Tme trend, Chna dummy Analyze orgn of TT and credt buyers UNFCCC (2010) 4974 (all) Sze, (busness clmate) (Unlateral), Smlar, Small Two stage estmatons for country effects: scale, (Chna dummy). 2 nd Predcted TT probablty based on estmatons stage: (Exports), Offcal wth country dummes regressed on country development assstance, characterstcs n a 2 nd stage Populaton, Tarff rates, Further estmatons wth a subset of countres to Knowledge stock, Democracy dentfy specfc barrers ndex, Renewable energy share Das (2011) 1000 (reg) No regresson analyss More narrow defnton of TT, Involvement of nternatonal consultant mproves lkelhood of TT Luo and Ye Sze, Smlar (2011) Marcon and Sanna- Randacco 889 (reg) EU emsson allowance prce, Consultant s carbon trader or from research nsttute, Developed regons dummy 715 (reg) Sze, Investment/CER, Project owner s consultant, Credt buyer s large (Prce of certfed emsson reducton (CER)), (Buyer s fnancal nsttute) Northwest Chna dummy, Smlar projects for wnd Technology and regon dummes, Smlar, Chnese PDD Only Chnese projects, focus on buyer/consultants, no controls for ndustres, nstead of year dummes the average annual prce of EU emsson allowances or the prce for CERs was ncluded Only Chnese non-hydro projects, analyss of technology provders and CER buyers, smlar projects postve but sgnfcant only when 4

6 (2012) consultant consultant, Sze of PDD consultant Schmd (2012) 3296 (all) Sze, (Busness clmate), (duraton), (Unlateral) Murphy et al. (forthcomng) 3530 (all) Sze, Imports, FDI, Tarff rates, Abatement cost, Transferred technology f, 2 nd stage: Offcal development assstance, Knowledge stock (Smlar projects), MFN tarff rates on envronmental goods, (R&D), (GDP) Smlar projects, Small scale, Captal formaton, Abatement potental, Technology base. 2 nd stage: Populaton, Share of renewables Technology and country dummes Technology and country dummes controllng for nteracton wth technologes, declnng trend over tme (ncreasngly negatve year effects) Analyss of dfferent TT types; educaton, unlateral and R&D only sgnfcant n specfcaton wthout technology and country dummes Two stage estmatons for country effects, year dummes show declnng effect over tme, Chna dummy n frst stage postve but not sgnfcant, some varables swtch sgn n one and two stage estmaton (per capta GDP) Zhang and 1509 (reg) GDP, Sze (Patent stock), Smlar Technology dummes, Only Chnese projects Zheng (2012) projects Internal rate of return a Observatons refers to number of projects, and all/reg. refer to all projects n the ppelne or only regstered projects, respectvely. b Drvers do not nclude specfc technologes, but technologes are often controlled for. The symbol "+" ndcates that regressors have a statstcally sgnfcant postve effect on TT, whle "-" a statstcally sgnfcant negatve effect on TT. Determnants shown n parenthess are not robust n the estmatons. c "Unlateral" denotes projects that started wthout a foregn partner. d "Annex I countres" refers to countres wth bndng emsson targets as lsted n Annex I of the Kyoto protocol. e "Smlar" refers to the number of projects n the same country/regon and the same technology. f "Transferred technology" refers to the number of applcatons by foregn patent holders relatng to the technology used by the CDM project. 5

7 Internatonal cross-secton estmatons wth country dummes dd not fnd a consstent coeffcent for Chna. As many of Chna s ntal projects ncluded TT, the coeffcent was postve n Hates et al. (2006), but negatve (yet not always sgnfcant) n later studes (UNFCCC 2010, Seres 2009). Some studes also analyzed the effects of dfferent roles that CER buyers and project consultants can fll. TT s more lkely n projects where PDD consultants are more closely connected to the project (e.g. through ownershp of the project) and less lkely when CER buyers are comng from fnancal nsttutons and thus have less drect nterest n the technology used. Unlke all other quanttatve studes mentoned above whch measured determnants for TT n CDM, Haščč and Johnstone (2011) focused on the effect of CDM (among other channels) on blateral TT n the wnd energy sector. 2 They found that wnd energy patents frst regstered n Annex I countres are more lkely to be also regstered n countres wth more wnd energy CDM projects. For Chna, ths effect s larger than for other mportant CDM host countres. They also found a negatve stock effect,.e., wth more CERs already ssued, the propensty to transfer wnd energy patents to CDM host countres declnes. Three studes analyzed CDM projects n Chna only (Luo and Ye, 2011; Marcon and Sanna-Randacco, 2012; Zheng and Zhang, 2012). Luo and Ye (2011) as well as Marcon and Sanna-Randacco (2012) emphaszed the role of partcpants n the market. For the lkelhood of TT n a CDM project t matters who acts as a project consultant and CER buyer. Ths confrms Das (2011) and Schroeder (2010) pontng out that there are several dstnct segments n the market for CDM projects. Zheng and Zhang (2012) emphaszed the role of the exstng knowledge stock whch has a negatve effect on TT. As domestc renewable technologes n Chna approach the global technologcal fronter, the potental for TT decreases. Also among the three studes on Chna, only Zheng and Zhang (2012) found a robust negatve agglomeraton effect from smlar projects at the natonal level. In general, fndngs from nternatonal cross-country studes seem to hold for the relatvely few ncluded varables descrbng provnce characterstcs. Ths paper therefore ams at extendng the analyss of Chnese projects by lookng at agglomeraton effects at the sub-natonal level and by nvestgatng the role of provnce-specfc capacty and capablty n R&D actvtes on TT n more detal than n prevous studes. Compared to cross-country studes, whch rely on cross-country varablty, ths paper looks at wthn-country dfferences. Thus, determnants found sgnfcant n an nternatonal context mght not apply here because all provnces are subject to the same polces, e.g. tarff rates. However, there reman dfferences between provnces n characterstcs, e.g. FDI flows and ther technologcal capabltes. 3 Data descrpton and Estmaton Strategy The nformaton of whether a gven CDM project encompasses TT s taken from the database underlyng UNFCCC (2010). PDD data has some lmtatons as ths s an ex-ante descrpton of the planned project. Furthermore mentonng TT n the PDD s not requred and there exsts no standard way of dong so. Therefore there s some potental of measurement error. The most pronounced error arses from projects that do not menton TT at all, namely, nether ts presence nor ts absence n a gven project. Prevous studes have ether nterpreted these as absence of TT or excluded the projects from the sample. Murphy et al. (forthcomng) carry out a follow-up survey to check for the accuracy of TT codng. Whle the codng for TT from PDD performs well for projects that ether state 2 Because of ths dfference to the other studes, we do not nclude ths study n Table 1. 6

8 presence or absence of TT, the results of the other group s mxed as many projects that do not menton TT n fact nvolve some form of TT. In ths study we opt for excludng projects wth unclear nformaton on TT. For Chna, ths excluson does not matter much because only about 10% of projects are coded as unknown TT. Results quanttatvely and qualtatvely hardly change when usng all observatons, wth one major excepton that we comment when llustratng our results. The TT data s merged wth CDM project specfc nformaton taken from the UNEP Rsø CDM/JI Ppelne Database (UNEP Rsø Centre, 2012). For Chna, we have 1799 matched projects that have entered the ppelne between January 1, 2005 and June 30, Data on provnce characterstcs nclude provncal GDP per capta, populaton sze, FDI engagement as proxy for openness, and R&D actvtes of dfferent nnovators. Provncal data are collected from the annual Chna Statstcal Yearbook and annual Chnese Yearbook of Scence and Technology for the research perod correspondng to the CDM database. Fgure 1: Number of CDM projects n dfferent provnces (a) and share of TT n CDM projects (b) (a) (b) Data source: authors complaton based on data from UNEP Rsø Centre (2012), and UNFCCC (2010). CDM projects are not evenly dstrbuted among provnces (see Fgure 1a). More than 100 projects are mplemented or projected n each Inner Mongola, Hunan, Schuan, Yunnan, and Gansu. These provnces have large potental for renewable energy projects lke hydro energy (Hunan, Schuan, Yunnan, and Gansu) or wnd energy (Inner Mongola and Gansu). Projects related to hydro and wnd energy consttute the majorty of CDM projects n these provnces and Chna n total. Because of dfferent geographcal features, dfferent provnces are more lkely to engage n dfferent technologes whch n turn unevenly nvolve TT. The share of CDM projects wth TT s thus very dfferent between provnces (Fgure 1b). Overall about 21% of the projects nvolve TT, but the share s hgher n coastal provnces, especally n the muncpaltes of Bejng, Tanjn, and Shangha (although they do not host many CDM projects). Part of the dfferences mght be due to geographcal characterstcs whch make dfferent project types more sutable for dfferent provnces, whch s of 3 We exclude two projects whch started n 2004 because these rather have to be judged as plot projects pror to the enterng nto force of the Kyoto protocol and the set-up of CDM related admnstratve structures. Out of the 1799 projects, we further exclude 5 projects mplemented n more than one provnce and can thus not be used n our econometrc strategy. By ncludng technology effects n the estmatons, 47 observatons must be dropped because all geothermal and N 2 O abatement projects contan TT, whle all afforestaton, reforestaton, solar energy, household energy effcency, ndustry energy effcency, and ol flarng reducton projects contan no TT. We are left wth 1752 observatons. 7

9 polcy relevance n tself. However t s also nterestng to gan more nsghts nto whether dfferent polces have a drect effect on the lkelhood of TT n CDM projects. We adopt an econometrc approach to hghlght such nformaton. In order to nvestgate the determnants of the TT of CDM projects, we construct a baselne model consstng of four groups of potental determnants for estmaton. The TT varable s bnary, therefore we apply a non-lnear approach and carry out probt estmates. The basc dea of our emprcal models can be presented as follows: y = * x β + ε (1) * where denotes ndvdual CDM projects, y s a latent varable that s related to the dependent varable of nterest TT, x refers to the exogenous varables that are expected to affect the TT of CDM projects (Secton 2) and ε represents a random error term. * The latent varable y s unobserved but s lnked to TT n CDM projects. More concretely, the dummy varable TT s equal to 1 (ndcatng the mplct occurrence of TT n the -th CDM project) f the latent varable s larger than zero and t s equal to 0 (no technology transfer) otherwse. Assumng a standard normal dstrbuton of the error termε, we end up wth probt models as follows for estmaton: Pr( TT * = 1 x) = Pr( y > 0 x) = Φ( xβ ) (2) wth Φ(.) referrng to a standard normal cumulatve dstrbuton functon. The dependent varable s equal to 1 when technology transfer was explctly documented to be carred out n the CDM applcaton documents and 0 otherwse. Ths varable and the explanatory varables dscussed below are descrbed n Table 2. Some basc statstcs are provded there as well. In our baselne models we focus on the followng four groups of project-specfc determnants. basc yr nd agg ) x = (x x x x (3) basc x conssts of three basc project characterstcs project sze n log (lnsze), whether t s a smallscale project whch s characterzed wth more smple documentaton procedures (smallscale) and whether t s a unlateral project,.e., started wthout foregn partners (unlateral). These three basc project characterstcs were n the prevous lterature found of sgnfcant relevance for determnng the probablty of TT of the CDM projects. yr The second group of project-specfc determnants, x, consders the tme effect. The startng year of the CDM projects s codfed n fve year dummy varables (year2006, year2007, year2008, year2009, and year2010), assumng the year of 2005 as reference year. We expect a larger TT probablty n earler CDM projects. Table 2 gves us some frst support for ths expectaton, where the share of TT s generally larger for the projects started n earler years. Consderng the fast 8

10 development experenced by Chna, t s lkely that the potental for technology catch-up n recevng provnces dmnshed over tme as these provnces reduced ther gap to the technologcal fronter. Table 2: Summary statstcs and varable descrpton based on the observatons from 2005 to 2010 Varable Descrpton Mean Std. Dev. Mn/Max %TT TT Dummy for TT / lnsze Project sze n log for expected 1000 CERs annually / smallscale Projects applyng smplfed modaltes and procedures for small scale / unlateral Projects wthout an Annex I Party letter of approval at the tme of regstraton / year2006 Year dummy for / year2007 Year dummy for / year2008 Year dummy for / year2009 Year dummy for / year2010 Year dummy for / hydro Hydro energy projects / wnd Wnd energy projects / eeown Energy effcency n own generaton projects / eesupply Energy effcency n supply projects / dstrbuton Energy dstrbuton projects / swtch Fuel swtch projects / mnemethane Mne methane projects / methaneavod Methane avod projects / landfll Landfll gas projects / hfcs HFC-23 destructon projects / cement Cement sector projects / provcount Number of projects already n the ppelne wth same technology and n the same provnce at project start /195 - regcount Number of projects already n the ppelne wth same technology and n the same regon (geographcally defned) at project start /634 - natcount Number of projects already n the ppelne wth same technology n Chna at project start /882 - techregcount Number of projects already n the ppelne wth same technology and n the same regon (technologcally orented) at project start /839 - gdp_pc real per capta GDP n RMB (n prces of 2000) / lnpopulaton Log of Populaton (populaton n 10,000) / trcf Total nvestment of (partally) foregn funded / companes n stock relatve to GDP rd_ml R&D expendture of companes as percentage / of GDP rd_unr R&D expendture of unverstes and research nsttutes as percentage of GDP / Data source: authors calculaton based on data from NBSC (2004a 2010a), NBSC (2004b 2010b), UNEP Rsø Centre (2012), and UNFCCC (2010). Note: Each varable has 1752 observatons (confned by the valdty of TT varable). Industry dummes, nd x, are thus consdered as our thrd group of determnants, to nvestgate possble ndustry effects on the presence of TT n CDMs. The reference group used n the estmatons s bomass energy, a category wth medum TT ntensty. As shown n Table 2, share of TT dffers strongly among projects from dfferent ndustres. Last but not least, we consder a potental agglomeraton effect of CDM projects on TT probablty by controllng for the amount of CDM projects whch were already n place n the same area and 9

11 ndustry at the tmng as the CDM project of focus started ( x agg ). We expect that the greater the number of already exstng projects n the same ndustry n the same area (agglomeraton), the more s the experence wth new technology and the smaller the potental for further TT (under the very lkely assumpton for Chna of on-gong technologcal catchng-up). In order to nvestgate whether the provncal boundary s too lmted for analyzng the agglomeraton effect, we replace the provnce-level count varable (provcount) by a regon-level count varable (regcount) whch s constructed n the same way as ts provnce-level counterpart. For ths we classfy the 31 provnces n Chna nto four regons followng the offcal classfcaton defned for regonal economc polcy: north-eastern, coastal, central, and western regon. As an alternatve regonal classfcaton we also aggregate the provnces nto four regons accordng to ther domnant technology n the observaton perod (wnd, hydro, energy effcency n own generaton, other). For comparson wth prevous studes, we also consder the naton-wde agglomeraton varable (natcount). We consder the four groups of ndependent varables n sequence for estmaton. In order to control for the robustness of our results, we further extend our model by ncludng two groups of provnce-specfc characterstcs, whle stckng to our estmaton strategy above. In ths way we can better take nto consderaton whether the consdered provnce-specfc characterstcs strengthen or weaken the probablty of TT n CDM projects, takng as gven the project-specfc characterstcs. prov gen rd x = ( x x ) (4) The group of general provncal characterstcs ( x gen ) conssts of three varables: the development level of provnces (real GDP per capta, gdp_pc), ther populaton sze n log (lnpopulaton) and ther openness to foregn nvestors n the world (trcf). 4 The second group of provnce specfc characterstcs ( x rd ) takes nto account the technologcal capabltes of the provnce consdered. It conssts of two varables, namely the R&D expendture of companes (rd_mle) and of unverstes and research nsttutes (rd_unr) as percentages of GDP. Dfferent types of nnovators may have dfferent strengths and focuses regardng nnovaton. Thus, ther nnovaton behavor mght affect the TT probablty n CDM projects to varyng extents. All sx provnce-specfc characterstcs are calculated as two-year movng averages. In other terms we use data at tmes t-2 and t-1 to calculate the average for the CDM project whch started at the year t. We do so n order to smooth out short-term fluctuatons and to deal wth potental endogenety ssues. It s often customary n fact to use past lags of varables as nstruments of current varables n many feld of economcs on the bass of a weak exogenety argument. However, here there s no theoretcal excluson restrcton not to nsert past varables drectly nto the model. Therefore we beleve we offer the best model specfcaton possble. For estmatons we consder frst the group of general provncal characterstcs and we later add n our emprcal models the R&D-related provncespecfc characterstcs. All models are estmated usng the provnce-clustered sandwch estmator. Ths relaxes the usual requrement that the observatons are ndependent and standard errors allow for ntra-group 4 Trcf refers to the total nvestment of (partally) foregn funded companes n stock relatve to GDP. We consder trcf as a proxy for a provnce s relatve openness to the world nvestors n our estmatons. 10

12 correlatons. Snce the probt models are non-lnear models, the estmated coeffcents n the results should not be nterpreted as margnal effects on the probablty drectly. They show us nstead the general drecton of the effects of certan explanatory varables on the TT probablty of the CDM projects. 5 4 Estmaton Results Estmaton results for the baselne models are presented n Table 3, whle we wll add provnce specfc factors n Table 4. We llustrate our results accordngly. In the appendx we report the same estmatons usng all observatons, codng unclear TT as no TT. Table 3: Project-specfc determnants of TT probablty of CDM projects (Baselne models) (1) (2) (3) (4) (5) lnsze 0.425*** 0.445*** 0.432*** 0.440*** 0.445*** smallscale unlateral year *** *** *** *** *** year *** *** *** *** *** year *** *** *** *** *** year *** *** *** *** *** year *** *** *** *** *** hydro *** *** *** *** *** wnd 0.439* 0.574** 0.489* 0.687** 0.643** eeown 0.661** 0.683** 0.684** 0.784*** 0.707** eesupply 1.328*** 1.253*** 1.290*** 1.157** 1.223*** dstrbuton swtch 2.049*** 1.979*** 2.019*** 1.912*** 1.959*** mnemethane 0.511* 0.501* 0.504* 0.483* 0.481* methaneavod 0.762* 0.745* 0.751* 0.694* 0.741* landfll 1.676*** 1.646*** 1.664*** 1.615*** 1.637*** hfcs cement provcount *** regcount natcount techregcount _cons Wald Ch *** *** *** *** *** obs Data source: authors' estmaton based on data from NBSC (2004a 2010a), NBSC (2004b 2010b), UNEP Rsø Centre (2012), and UNFCCC (2010). Note: ***/**/* sgnfcant at 1%/5%/10% (two-taled tests). In Table 3, we start our estmaton wth a smple model (1) only ncludng basc project characterstcs, tme, and ndustry effects. We fnd a postve and hghly sgnfcant coeffcent for the project sze whch s commonly found n the lterature (see Table 1). The coeffcent of small scale s negatve but not sgnfcant. Some earler studes (see Table 1) found that unlateral projects (.e., wthout a known buyer for CERs) were less lkely to nclude TT. We cannot confrm ths, as a lkely result of project consultants' acton, who nowadays can also serve as technology brokers (Das, 2011). Therefore, the lack of statstcal sgnfcance of the "unlateral" varable can descend from the fact that the absence of a drect connecton wth foregn CER buyers does not necessarly preclude TT. 5 We addtonally compute margnal effects (elastctes) for the varables of man concern for our analyss, namely sgnfcant agglomeraton varables and those regardng provncal characterstcs (End of Secton 4). 11

13 In the smple specfcaton as well as n all the extensons we fnd a hghly sgnfcant declnng trend of TT over tme. Ths trend cannot be explaned by a possble shft n CDM composton towards projects wth ether less or no TT, because ths would be captured n the technology effects. The coeffcents on the tme dummes should nstead be better nterpreted as a general declne n TT n CDM n Chna. The ncreasngly negatve year dummes (relatve to the 2005 projects) show a pronounced drop already for 2006 projects. Varous factors can explan ths trend over tme. When CDM was ntroduced, the Chnese government dd not promptly promote t. However, foregn frms, lookng for CO 2 reducton opportuntes, were more actve n the sector and they also fostered TT. Later the Chnese government became more proactve by buldng nsttutons and spreadng relevant nformaton, whch led to the entrance of many nexperenced project consultants of lesser qualty and wth lttle experence n nternatonal TT n 2007 (Schroeder, 2010). As a consequence, the declnng tme trend contnued. Marcon and Sanna-Randacco (2012) attrbute ths fndng to a general trend of technologcal progress n Chna - not lmted to CDM projects - whch made TT less lkely over tme. However, an alternatve explanaton cannot be ruled out based on our estmatons. Consultants mght have learnt that TT s not a necessary condton for acceptance of CDM projects. They need to demonstrate that a project would not have been carred out wthout the CDM. However, when overcomng fnancal and admnstratve barrers to adopt foregn technology s not necessary to demonstrate ths addtonalty, consultants mght prefer less bureaucratc projects relyng on domestc technology. As expected, dfferent technologes are not equally lkely to nclude TT. As our reference technology for the analyss we use bomass. Hydro energy projects rarely nclude TT, as ths s a mature technology used n Chna for a long tme. Chna even exports ths technology to be used n CDM projects n other countres (Murphy et al., forthcomng). Contrary to hydro energy, the other wdely used renewable energy technology n the CDM, wnd energy, has a hgher lkelhood of TT. The lkelhood for TT n energy dstrbuton, destructon of hydrofluorocarbons (HFCs), and cement projects s not statstcally dfferent from bomass energy. Partly ths s due to the small number of projects n the bespoken categores. The coeffcent of cement projects s negatve but not sgnfcant as a possble result of the fact that cement technology, now wdely used n Chna, was mported from Japan before CDM projects came nto exstence (Wang, 2010). Ths technology, therefore, s lkely to be close to the technologcal fronter nowadays n Chna. Capturng methane from landflls, mnes, or waste water (most projects n the category methane avodance ) on the other hand s lkely to nvolve TT; ths s also a prorty area as specfed by the government. Schneder et al. (2008) report that n these projects mostly end-of-ppe technologes are used and projects are often ntated by nternatonal partners. Fuel swtch projects manly consst of gas fred power plants whch were bult nstead of coal fred plants. As these power plants are relatvely large, t s lkely that at least n some areas there s TT. Ths also holds for mprovements of energy effcency n own use (eeown) and supply (eesupply). Energy effcency s also defned as prorty area as specfed by the government. In general, there s evdence that more advanced technologes and some technologes that are supported by the government (ths s not the case for all energy technologes) are more lkely to nvolve TT. Ths broadly confrms the conclusons by Dechezleprêtre et al. (2008), statng that hgh technologcal capablty favors TT n energy sector and chemcal ndustry. 12

14 We can confrm negatve agglomeraton effects from smlar ether already planned or exstng close-by projects n the same provnce (2). Whle ths fndng s robust n nternatonal cross-country studes, Zhang and Zheng (2012) also fnd ths for Chna but Marcon and Sanna-Randacco (2012) observe ths only for wnd energy projects. However, both studes consder the natonal level, whle our varable provcount captures the sub-natonal level. When we move from the provncal level to the regonal level (3), we do not fnd evdence for sgnfcant agglomeraton effects anymore. The same happens when ether consderng the natonal level (4) or when not usng the admnstratve regons, but regons constructed on the bass of the sutablty for dfferent technologes (5). 6 Here, we group a provnce nto one of four (wnd, hydro, eeown or other) regons accordng to the technology wth the most projects. Therefore we can conclude that local agglomeraton effects can be better captured at the provnce level, whch s plausble gven the sze of Chnese provnces. Table 4: Extended models consderng provnce-level determnants of TT probablty of CDM projects (1) (2) (3) (4) lnsze 0.442*** 0.427*** 0.442*** 0.454*** smallscale unlateral year *** *** *** *** year *** *** *** *** year *** *** *** *** year *** *** *** *** year *** *** *** *** hydro *** *** *** *** wnd 0.514** 0.510** 0.554** 0.610** eeown 0.678** 0.656** 0.652** 0.666** eesupply 1.262*** 1.228** 1.224** 1.184** dstrbuton swtch 1.986*** 2.053*** 2.029*** 1.982*** mnemethane 0.578* * 0.577* methaneavod 0.750* landfll 1.687*** 1.691*** 1.711*** 1.676*** hfcs cement provcount ** gdp_pc lnpopulaton trcf 0.066** 0.076** 0.062** rd_mle rd_unr 0.380*** 0.418*** 0.368*** _cons Wald Ch *** *** *** *** obs Data source: authors estmaton based on data from NBSC (2004a 2010a), NBSC (2004b 2010b), UNEP Rsø Centre (2012), and UNFCCC (2010). Note: ***/**/* sgnfcant at 1%/5%/10% (two-taled tests). Gong beyond project specfc characterstcs, we add some provnce specfc drvers n Table 4. Estmatons wth provnce fxed effects were less nformatve because just a few provnce dummes 6 When usng all observatons ncludng unclear TT, the count varables n models (4) and (5) are sgnfcant at the 5% level (see Table A1). Ths s however not our preferred specfcaton because TT s measured wth more uncertanty. 13

15 were sgnfcant. 7 Whle ths ndcates that provnces are, n general, relatvely smlar when accountng for CDM project characterstcs, t mght not be so when consderng specfc aspects of thers. Furthermore, n ths way, we can offer comparson wth nternatonal studes that consdered smlar varables. Model (1) ncludes macro varables at the provnce level. Openness, measured by trcf, has a postve coeffcent, confrmng earler studes concludng that nteracton wth foregn frms va FDI fosters TT. Model (2) extends the exstng lterature by addng dfferent types of R&D to GDP ratos. In the exstng lterature only Schmd (2012) used R&D to GDP ratos n a cross-country sample. Not usng a dfferentated measure for R&D as ths paper does, she found nsgnfcant coeffcents wth varyng sgns. When addng the R&D varables to the equatons, only R&D actvtes of unverstes and research nsttutes have a postve and hghly sgnfcant coeffcent. Ths knd of research, whch s often basc and not appled research, could very well be a complement to TT because these actvtes foster hgher educaton and a general level of technologcal understandng. Other types of R&D (n frms) on the other hand yeld negatve, yet nsgnfcant coeffcents. In our vew ths means that a hgher level of general-purpose knowledge can foster TT, whle more appled knds of t can rather substtute for TT. The latter fndng s common n the lterature, for nstance UNFCCC (2010) and Zhang and Zheng (2012) fnd a negatve effect of knowledge stocks on TT. In these studes, the knowledge stock was approxmated by a patent stock n renewable energy technologes,.e., much closer towards the technology n queston. Thus the negatve coeffcent there (smlar to the negatve but not sgnfcant coeffcent for R&D n frms) can be seen as an ndcator for substtuton between exstng specfc knowledge and TT. The coeffcents for the varables n basc x, yr x, and nd x are robust to the ncluson of addtonal varables n the extended models. In partcular, model (4) captures all the aspect we stressed n our analyss: agglomeraton, FDI and R&D effects. Puttng focus on the role of provnce-specfc varables, we compute for ths model the three correspondng elastctes at mean values. Provcount has an elastcty of -0.29, trcf of 0.31 and rd_unr of The frst two are statstcally sgnfcant at the 5% level, whle the thrd at the 1% level. 8 5 Conclusons Our estmatons on the determnants of TT n Chnese CDM projects largely confrm the exstng lterature, yet also brng attenton to new aspects, such as the role of agglomeraton effects and specfc types of R&D. These varables are of partcular polcy relevance because they could be nfluenced by polcy makers more than other determnants of TT. Wth regard to standard determnants lke project sze or technology categores, we can confrm results from prevous cross-country studes and studes for Chna. More advanced technologes and larger projects are more lkely to nvolve TT. Ths s very ntutve gven an extra fxed cost of 7 Ths hampers a two-stage-estmaton strategy as n UNFCCC (2010) or Hates et al. (2012). 8 For ths specfcaton of ours we also carred out a Lagrange multpler normalty test of the resduals after Dallo (2010) obtanng a statstc of 0.07 wth a p-value of Normalty was therefore not rejected. Furthermore, we mplemented for Model (4) also a sem-nonparametrc (SNP) estmator after Gallant and Nychka (1987) and De Luca (2008). Results hardly changed. What s more mportant s that a lkelhood rato test of the probt model aganst SNP reported a statstc of 0.27 wth a p-value of 0.67, favorng the former over the latter one. Skewness and kurtoss of the dstrbuton of the resduals were very close to the Gaussan values 0 and 3, beng 0.17 and 3.06 respectvely. 14

16 mplementng TT that only pays off when the project s suffcently large. We also fnd a declnng trend of TT probablty over tme. The specfc pattern of changes n the lkelhood of TT n certan years can be well explaned by polcy developments that affected the partcpants n the markets for CDM projects n the affected years. Whle the trend can also be observed n nternatonal studes, the partcular values can best be explaned on a country level, takng nto account polcy developments specfc to the country. Regardng the macro-economc factors, we fnd that FDI stocks and R&D at unverstes and research nsttutes can foster TT. The analyss of dfferent types of R&D rather suggests that basc R&D nstead of appled R&D s complementary to TT of CDM projects. When lookng at the macroeconomc determnants of nternatonal TT, the provnces along the coast generally have better prerequstes for TT. Improvng the condtons for TT n other provnces by fosterng e.g. R&D at unverstes and FDI can encourage more TT to support knowledge-based economc growth n these provnces n the future. The negatve agglomeraton effect provdes evdence for the presence of an externalty, because the lkelhood of TT of a gven project s nfluenced by prevous projects applyng the same technology. Ths agglomeraton effect s found on the provncal rather than on the natonal level. The presence of an externalty could serve as justfcaton for polcy nterventon. Wth the present dataset we can however not clearly determne the channel of the agglomeraton effect. It s lkely that the channel s through learnng from TT n prevous projects. In other words, local stakeholders may already learn qute a lot through obtanng technologes transferred from the exstng CDM projects. The room for CDM latecomers for further TT s then restrcted. It s, however, also possble that carryng out CDM projects n a provnce where a large pool of technologcally smlar CDM projects exsts may ntensfy competton among the project holders whch makes t dffcult for transparent cooperaton wth local stakeholders, thus reducng the TT lkelhood. In both cases, polcy nterventon may be justfed to encourage especally the technologcally dversfed CDM projects and project holders cooperaton wth local stakeholders. But before callng for support of TT, t s mportant to note that fosterng TT as such s not necessarly an economc goal that should be strved for at any cost. TT can be more costly than developng local technologes. If the costs of TT exceed the benefts, t mght be neffcent to artfcally foster TT. To measure the benefts and costs of TT, one would need to carefully take nto account the potental postve/negatve externaltes resultng from the agglomeraton of CDM projects. A complete analyss s beyond the scope of ths study. Whle we assess determnants for TT n CDM projects n Chna up to 2010, t s dffcult to make projectons for the future. Most of the emsson reductons from Chnese CDM projects n the sample were ntended for use n the EU emsson tradng scheme (ETS). Startng from 2013, CERs from new projects to be used n the EU ETS have to come from least developng countres (Drectve 2003/87/EC, Artcle 11a (4)). Ths led to a collapse n the number of new Chnese projects after Whle 1812 projects were regstered n 2012, only 42 new projects were regstered n the frst half of It s more lkely that Chnese emsson reducton certfcates wll be used n the new Chnese emsson tradng systems. Wthout the drect ncentves from foregn frms to gan access to CERs, t s also lkely that TT wll declne. On the other hand, domestc TT could ncrease, at least n some technologes. A reason for ths s that the plot emsson tradng schemes are located n the more advanced provnces and there s a technologcal gap to the nland provnces. 15

17 References: Das, K. (2011). Technology transfer under the Clean Development Mechansm: An emprcal study of 1000 CDM projects (Workng Paper 014, The Governance of Clean Development Workng Paper Seres). School of Internatonal Development, Unversty of East Angla, UK. de Connck, H.C., Haake, F., & van der Lnden, N. (2007). Technology transfer n the Clean Development Mechansm. Clmate Polcy, 7, Dechezleprêtre, A., Glachant, M., & Ménère, Y. (2008). The Clean Development Mechansm and the nternatonal dffuson of technologes: An emprcal study. Energy Polcy, 36(4), Dechezleprêtre, A., Glachant, M., & Ménère, Y. (2009). Technology transfer by CDM projects: A comparson of Brazl, Chna, Inda and Mexco. Energy Polcy, 37(2), De Luca, G. (2008). SNP and SML estmaton of unvarate and bvarate bnary-choce models. Stata Journal, 8, Dallo, I. A. (2010). SKPROBIT: Stata module to perform Lagrange Multpler Test for Normalty for Probt model, Boston College Department of Economcs. Haščč, I., & Johnstone, N. (2011). CDM and nternatonal technology transfer: emprcal evdence on wnd power. Clmate Polcy 11(6): Hates, E., Duan, M., & Seres, S. (2006). Technology transfer by CDM projects. Clmate Polcy, 6, Hates, E., Krkman, G. A., Murphy, K., & Seres, S. (2012). Technology Transfer and the CDM. In D. G. Ockwell & A. Mallett (Eds.), Low carbon technology transfer. London: Earthscan. Gallant, A.R., & Nychka, D.W. (1987). Sem-nonparametrc maxmum lkelhood estmaton. Econometrca, 55, Luo, K., & Ye, R.D. (2011). Analyss of low-carbon technology transfer by CDM: An emprcal study from Chna (n Chnese). Economc Geography, 31(3), Marcon, D., & Sanna-Randacco, F. (2012). The clean development mechansm and technology transfer to Chna. Bank of Italy Occasonal Paper No 129. Murphy K, Krkman, G.A., Seres, S., & Hates, E. (forthcomng). Technology transfer n the CDM: an updated analyss. Clmate Polcy. NBSC (2004s-2010s). Natonal Bureau of Statstcs Chna - Chna statstcal yearbook. Bejng: Chna Statstcs Press. NBSC (2004b-2010b). Natonal Bureau of Statstcs Chna - Chna statstcal yearbook on scence and technology. Bejng: Chna Statstcs Press. Schmd, G. (2012). Technology transfer n the CDM: The role of host-country characterstcs. Clmate Polcy, 12,

18 Schneder, M., Holzer, A., & Hoffmann, V.H. (2008). Understandng the CDM's contrbuton to technology transfer. Energy Polcy 36(8): Schroeder, M. (2009). Varetes of carbon governance: Utlzng the Clean Development Mechansm for Chnese prortes. The Journal of Envronment & Development 18(4), Seres, S., Hates, E., & Murphy, K. (2009). Analyss of technology transfer n CDM projects: An update. Energy Polcy, 37, Teng, F., & Zhang, X. (2010). Clean development mechansm practce n Chna: Current status and possbltes for future regme. Energy, 35(11), UNFCCC. (2010). Analyss of the contrbuton of the Clean Development Mechansm to technology. Retreved from UNEP Rsø Centre (2012), CDM Ppelne Database (as of 1 October 2012). Zheng W., & Zhang, J. (2012). Research on the nfluencng factors of mtgaton technology transfer n the clean development mechansm: A logt model study of technology transfer n Chnese provncal CDM projects (n Chnese). Studes n Scence of Scence, 30(12), Wang, B. (2010). Can CDM brng technology transfer to Chna? An emprcal study of technology transfer n Chna s CDM projects. Energy Polcy, 38(5),

19 Appendx: Table A1: Baselne models (observatons wth unclear TT ncluded n the estmaton and coded as no TT) (1) (2) (3) (4) (5) lnsze 0.362*** 0.381*** 0.373*** 0.383*** 0.384*** smallscale unlateral year *** *** *** *** *** year *** *** *** *** *** year *** *** *** *** *** year *** *** *** *** *** year *** *** *** *** *** hydro *** *** *** *** *** wnd ** 0.410* 0.652** 0.567** eeown 0.538** 0.565** 0.575** 0.702** 0.595** eesupply 0.877** 0.798** 0.820** 0.666* 0.761** dstrbuton swtch 1.255*** 1.224*** 1.239*** 1.195*** 1.217*** mnemethane methaneavod landfll 1.441*** 1.421*** 1.429*** 1.390*** 1.415*** hfcs n2o 1.790*** 1.785*** 1.783*** 1.775*** 1.780*** cement provcount *** regcount natcount ** techregcount ** _cons ** ** Wald Ch *** *** *** *** *** obs Source: authors estmaton based on data from NBSC (2004a 2010a), NBSC (2004b 2010b), UNEP Rsø CDM/JI Ppelne Database, and UNFCCC (2010). Notes: ***/**/* sgnfcant at 1%/5%/10% (twotaled tests). Not our preferred specfcaton because TT s measured wth more uncertanty. 18