Market and Non-market Policies for Renewable Energy Diffusion: A Unifying Framework and Empirical Evidence from China's Wind Power Sector.

• Author: Liu, Yang

INTRODUCTION

Increasing environmental and energy concerns can be addressed by accelerating technological change around the world. A technology can significantly impact an economy only if it is widely adopted by producers and accepted by consumers. Delays in deployment of low-carbon technologies could rule out the cost effectiveness of global climate policy (IEA 2015). Also, productivity growth has slowed down over the 2000s, partly owning to a slowdown in diffusion of global frontier innovations (Andrews et al. 2015). The question remains open--How will a renewable energy technology, once introduced, diffuse at a reasonably rapid pace?

The wind power sector in China provides a stylized fact. Although China had almost no wind power capacity in 2001, the country has led the global wind market with the highest installed capacity since 2010. This seemingly accessible wind technology did not diffuse to all countries but rather showed two deployment paths in the past decade. While most countries have failed to accelerate wind technology diffusion, China's wind energy has been surging. How could China have kept the technology diffusion so rapidly? What are the quantitative effects of various driving forces?

The literature has identified two groups of driving forces behind technology diffusion. One group includes market-based forces. Certain economic instruments can provide financial incentives to potential technology adopters by correcting market failures, i.e. allowing the adopters to explicitly obtain social net benefits associated with renewable technologies. The other group of driving forces is non-market based from a systematic perspective. For example, a specific institutional and regulatory framework may induce technological change. A comprehensive theoretical and empirical framework is required to investigate market- and nonmarket-based forces of renewable technology diffusion to support decision making in the choice of policy instruments.

In this article, we provide such a framework by developing a theoretical model that considers both groups of driving forces in the technology diffusion literature. Then, we validate the model with historical data derived from 1207 Chinese wind projects in the Clean Development Mechanism (CDM) (1) over the period 2004-2011. Finally, we numerically simulate the pathways of optimal production subsidies for maximizing social welfare associated with the wind power sector in China.

1. BRIEF LITERATURE OF TECHNOLOGY DIFFUSION

   Technology diffusion is the process of gradual adoption of a new technology by an economy as defined by the well-known Schumpeterian trilogy of technological change (Schumpeter, 1934). This process is generally analyzed within two theoretical frameworks: nonmarket intermediated (or epidemic) and market intermediated (or pecuniary) approaches.

   Nonmarket approach relies on an analog to the spread of an epidemic. The more firms/people are "infected" (those that have adopted the technology), the more likely the others will also be "infected". Adoption occurs once potential adopters become aware of the new technology. Increasing spread of information between previous and potential adopters reduces the uncertainty surrounding the technology and leads to further rapid adoption. Earlier works used probability density functions and Bass models to develop the concept of information acquisition (Mansfield 1963; Bass 1969, 2004). Bass diffusion models are typically applied to consumer decision-making and less to firm decision-making. All these epidemic-type models specify an S-shaped diffusion curve. (2) Recently, this social contagion is also discussed as peer effects (Gordon et al. 2014; Manski 1993). With the help of a disaggregate dataset of daily residential solar-panel adoption in California, Bollinger and Gillingham (2012) estimate the magnitude of peer effects by allowing for better identification of the peer group.

   The epidemic effect is likely to be systemic. A regulatory framework and national innovation system are important to provide a long-term view with clear milestones, reduce uncertainty and establish credibility. National systems of innovation (NSI) and regulatory instruments are key factors to shape and convey this epidemic effect. The concept of the NSI was developed successively by Freeman (1987), Lundvall (1992), Nelson (1993), and Metcalfe (1995). Their definitions of the NSI share some common points. They all emphasize on the network of institutions whose interactions determine the performance of technology development and diffusion, and the coordinating role of the government in influencing these interactions. A windfarm project lifecycle often involves multi-stakeholder cooperation. (3) The interactions of these institutions will be crucial to determining the speed of wind technology deployment. Ru et al. (2012) provide a good review on China's wind technology innovation pathways, as well as policy and market frameworks at different stages of its maturity. (4) Mandatory requirements, obligation schemes or voluntary approaches can also help strengthen this epidemic effect. The Chinese government did encourage electricity generators to include a minimum share of clean energy in their output mix, even though these goals were not often associated with a penalty in case of non-compliance. (5)

   Unlike the epidemic models assuming that potential adopters will use the technology once they learn about it, a few models focus on the market-intermediated effects. The technology adoption is modeled as an individual choice based on profitability consideration. Therefore, it is the expected net gain rather than information acquisition that determines the adoption decision. Three profitability driven effects are identified in the literature: rank effect, stock effect and order effect (Karshenas and Stoneman 1993; Geroski 2000; Hoppe 2002). (6)

   The rank effect models, also known as Probit models, rank firms in terms of the benefit from technology adoption, mostly determined by firm's heterogeneous characteristics such as firm size, age, capital structure, learning and search costs, switching costs and opportunities costs. Those firms with the highest ranks adopt the technology earlier than others.

   The game-theoretical models suggest that the stock effect and order effect may negatively affect technology diffusion. The stock effect assumes that the benefit to the marginal adopter of a new technology decreases as the number of previous adopters increases (Karshenas and Stoneman 1993). Adoption of a cost-reducing process technology could lead to more production by all firms in the industry, thereby lowering prices in the output market and stimulating demand for the products. Consequently, for any given cost of technology acquisition, a number of adopters may suffer losses if adoption is too wide to keep a reasonable supply of their products (Reinganum 1981). The order effect results from the assumption that the return to a firm from adopting new technology depends upon its position in the order of adoption, with high-order adopters achieving a greater return than low-order adopters (Karshenas and Stoneman 1993). The order effect is usually related to the first-movers that can obtain prime geographic sites or preempt the pool of skilled labor. High-order adopters can face less competition and gain greater benefits, therefore their decisions affect the adoption dates of low-order adopters (Fudenberg and Tirole 1985). However, it is worth noting that later adopters can potentially benefit from improved performance and reduced cost of a technology as second-mover advantages (Rosenberg 1976; Hoppe 2000).

   In the context of China's renewable energy sector, the order effects relate to first-mover advantage through control of a number of resources. Early adopters may benefit from the most favourable land and wind conditions. They may receive a higher feed-in tariff because a periodic tariff degression is expected to be implemented by the regulator. They can also receive carbon revenue through the CDM. Early adopters can access to the locations with a higher emission baseline enabling to claim more carbon credits. In a regulated electricity market, the stock effects generated through the output market may be less pronounced unless the variation in electricity sale prices is contained by a long-term feed-in tariff agreement. However, grid integration may still raise a serious concern due to the intermittency and non-dispatchable nature of wind energy. In fact, the Chinese grid constraint resulted in an abandon of a significant part of wind electricity. This could trigger the expectation on revenue loss of the wind investors. The rank effect, associated with firms' specific characteristics such as size, age, and capital structure, is mostly represented by the capital costs of a renewable project.

   The theory cannot predict the role of precedent adoption unambiguously, with stock and order effects having a negative impact and epidemic effects by contrast a positive one (Karshenas and Stoneman 1993). The net impact of precedent adoption on later adopter behavior must be treated as an empirical question. This article contributes to the literature from the perspective of theoretical method and analysis scope. The majority of literature on technology diffusion involves industrial and financial sectors. (7) Explicit modelling renewable energy diffusion is less common.

   Previous empirical analysis has been inconclusive. Existing literature is unanimous in finding that adoption decision is positively correlated with firm size and epidemic effect. (8) On the contrary, evidence on stock and order effects is mixed. Depending on the characteristics of technology and output market structure, Mulligan and Llinares (2003) and Hannan and McDowell (1987) find opposite impact of precedent adoption in the technology diffusion process, although their competitive models do not attempt...