Bridging renewable electricity subsidy with carbon offset scheme: What evidence from wind capacity diffusion in China?

Transcription

1 Bridging renewable electricity subsidy with carbon offset scheme: What evidence from wind capacity diffusion in China? Department of Economics, Ecole Polytechnique, France School of Management, Harbin Institute of Technology, China 1

2 Overview Motivation Literature Model Data & Econometric issues Results Conclusion & Policy implications 2

3 Motivation (1) Overview on wind capacity installations in China and in the world (unit: MW) Capacity MW Top 10 cumulative capacity Dec MW Ye a r MW A nnual ins talled c apac ity Cum ulative ins talled c apac ity A nnual CDM s upported c apac ity Cum ulative CDM s upported c apac ity MW Multi-policies environment Concessions bidding&fit Fixed FiT MW MW MW Clean Development Mechanism (CDM ) EU post 2012 CDM directive Source: Own elaboration Source: Global Wind Energy Council, 2012 Research question: Determinants of diffusion of wind technology in China: To which extent do CDM and FIT contribute? Interactions of CDM and FiT: Are there trade-offs between the two policies? Manipulation of FiT vis-à-vis CDM: Is the FiT tactically selected to obtain the CDM support? 3

4 Motivation (2) A simple numerical illustration of the financial impact of FIT and CDM on Chinese wind projects: Key project parameters Value Electricity generation (Mwh) Installed capacity (MW) 50 Number of hours of operation (h) Emissions reductions (tco2e) Emissions factor of the grid(tco2e/mwh) Investment cost (Million Yuan) 489 O&M cost (% of investment cost) 2.4% Debt (% of investment cost) 80% Loan rate 5.94% Loan payment term(yrs) 12 Depreciation(yrs) 12 Residual value (% of investment cost) 5% Lifetime(yrs) 20 No policy CDM only FiT only FiT policy Value Feed-in tariff (Yuan/kWh) 0.58 Coal generated electricity price (Yuan/kWh) 0.40 FiT premium 0.18 Electricity revenue (Million Yuan/yr) CDM policy Value Carbon revenue (Million Yuan/yr) CER price (Yuan/tCO2e) 100 CDM eligibility hurdle rate A FiT level in the case of more than 0.65 Yuan/kWh would remove the project from the eligibility for the CDM support. How can renewable electricity subsidy interact with carbon offset scheme? Does the project reduce CO2 emissions? National policy may affect the baseline scenarios. Is the project business-as-usual without the CDM? National policy may make a project profitable without the CDM. FiT+CDM IRR (20 yrs) <1% 4.34% 6.21% 8% 9.15% 4

5 Literature To date, limited analaysis lead to mixte conclusions: o Empirical literature: Pechak et al. 2011; Yang et al. 2010: Cost-benefit analysis or stochastic simulation approaches show that the income from carbon credits is not the primary reason for projects development. Dechezlepretre et al. 2008; Schneider et al. 2008; Seres et al ; Haščič et al. 2011, Doranova et al. 2010: This literature provides some support on the role of the CDM in international technology transfer, but not in technology diffusion. o Theoretical literature: Fischer 2005; Strand 2011: The CDM can create a distortion with the endogenous environmental policy in the countries hosting offset projects mainly due to the baseline determination challenges Hagem 2009, Glachant and Meniere 2011: Depending on the presence of learning spillovers, additionality stringency and product market characteristics, the CDM may yield a higher incentive and social welfare than a standard cap&trade. This study aims to fill the analysis gap on the effectiveness of FiT and CDM policies in changing the technology adoption behaviors in developing countries. 5

6 Model Question 1: what are the main drivers of wind power deployment in China? A rational wind investor will maximize profits by choosing the level of wind power capacity at the point where marginal revenue equals marginal cost. Benefits streams: electricity revenue + coal savings + carbon revenue Cost streams: capital cost + CDM cost Electricity revenue Load factor FIT price Coal savings Coal price Carbon revenue CER price Emission factor Capital cost Cost/kW Market share of domestic turbines manufacturers CDM cost Approval timeline Regulation change Dummy ( ) Three channels of CDM impact are assessed: CER price : spot price observed in the carbon market. CER quantity determined by the emission factor of the grid into which is fed the wind generated electricity. CDM approval process efficiency: average days from public comments to registration for CDM. Question 2: is the FiT policy influenced by the level of CDM subsidy? 6

7 Data A panel data of 30 provinces over the period based on 1207 Chinese wind CDM projects registered and in the validation process on the public CDM website. Description of data Data sourced from detailed project information: Installed capacity (MW), Unit investment cost (1000 Yuan/kW), FIT price (Yuan/kWh), Plant load factor(%), Emission factor of the electricity replaced by the project(tco2e/mwh) Data sourced from the country level information: Localization rate of wind turbines manufacturing(%), Coal price (Yuan/ton) Steel price (Yuan/ton) Data sourced from carbon market information: CER price (euros/tco2e), Average days from public comments to registration for CDM project (days) 7

8 Econometric issues Main calibrations of panel data : Fixed effects model or random effects model : Hausman test => presence of unobserved province effects correlated with the regressors => fixed effects model is chosen. Wooldridge test for autocorrelation in panel data (Wooldridge 2002)=> presence of autocorrelation between remaining error terms Modified Wald test for groupwise heteroskedasticity in fixed effect regression model=> presence of heteroskedasticity The Pesaran's test of cross-sectional independence=>presence of cross-sectional dependence Test the endogeneity of FIT price, emission factor of the electricity system, capital costs and wind capacity=>instrumental Variable estimator (IV) : steel price, steel price, global installed wind capacity and benchmark interest rate of the Chinese central bank=> no endogeneity problem From the above tests, fixed-effects regression model with Driscoll and Kraay standard errors is chosen to separately estimate two equations, because this method is able to control for autocorrelation, heteroskedasticity and cross-sectional dependence. 8

9 Results Estimation results on determinants of wind technology diffusion : * Significant at 10% level ** Significant at 5% level *** Significant at 1% level Three variables of the CDM impact Emission factor of the electricity system: a 1% increase will imply 4% growth in the wind power capacity CDM approval timeline: the coefficient is small and only significant at the 13% level. CER price: the impact is not significant and its coefficient is negative. FiT impact FiT price: the coefficient is significant but negative. Impact of wind project costs Capital costs of wind projects: a 1% reduction will induce 1.75% growth in the wind power capacity Market share of domestic wind turbines manufacturers: a 1% increase will bring 1.67% growth in the wind power capacity. Innovation effect Plant load factor: the coefficient is significant but relatively small. Regulatory change Dummy : the regulations impact since 2009 is 21% higher than that in the absence of these regulations. Coal savings effect Coal price: the impact is not significant. 9

10 Results : Estimation results on FiT determinants All of three variables representing the CDM impact are positively and non-significantly correlated with FIT prices. The FIT prices are significantly correlated, sorted by the importance, with Capital cost of wind projects Coal price Market share of local turbines manufacturers Plant load factor Wind power capacity. * Significant at 10% level ** Significant at 5% level *** Significant at 1% level 10

11 Conclusion The CDM is a primary determinant to wind technology diffusion in China. However its impact is not related to the price signals observed in the carbon market. This causality is established via carbon intensity of baseline energy generation that determines the amount of carbon credits to be issued. The opposite correlations of FiT and CDM with wind capacity deployment show potential tradeoffs between two policies to maintain the level of subsidy necessary to a break-even point. The FiT policy is not tactically determined by the CDM credit price in the Chinese experiment. The impacts of CDM and FiT are supported by a favorable environment in terms of wind project costs reductions, industrial manufacturing capacity and technology innovation. 11

12 Policy implications The climate offset mechanism undertaken by developed countries does induce the technological change in developing countries. These spillovers effects are more policy-driven than price-driven for now. The dynamic efficiency of renewable electricity subsidy might be incompatible with the concepts of offset scheme. A coordinated mechanism at sectoral level is needed to address potential tradeoffs between international and domestic subsidies. With regard to a homogeneous sector such as wind power, the results of this study suggest that technology penetration rate and emission factor of the electricity system could be efficient monitoring parameters to set up consistency between climate finance and technology deployment at policy level. 12