technological upgrading and productivity growth by opening opportunities and stimulating supplies of capital and skills. China‘s establishment of special economic zones, followed by a range of support by national and local governments for further industrial deepening in its three major urban/industrial agglomerations (the Pearl River Delta centered on Shenzhen, Dongguan and Foshan, the Yangtze River region around the Shanghai-Suzhou axis, and the Bohai region in the vicinity of Beijing and Tianjin) and in a number of inland cities (including the footwear cluster in Chengdu and the Wuhan opto-electronics cluster) highlights how a mix of instruments can be employed together, including support to science parks and extension services, encouragement of local universities to deepen industrial linkages, attracting a major local or foreign anchor firm that can trigger the in-migration of suppliers and imitators, and above all dense transport and communication connectivity infrastructure (Yusuf, Nabeshima and Yamashita 2008).

# **4  OUTSTANDING ISSUES**

Looking forward, there remains an important policy research agenda to better understand both evolving patterns of green innovation and the relative effectiveness of appropriate mixes of policies in different technological and institutional contexts.

Regarding patterns of green innovation, there is need for better data collection on green innovation. In particular, there is no direct evidence on adoption and adaptation of green technologies. There are almost no data measuring BoP green innovations to meet the needs of poor consumers. Firm-level surveys could be a good source to better understand what is taking place on the ground, with the recent EU Community Innovation green module a good starting point for more systematic inquiry across developing countries. More generally, it would be useful to better monitor public and private inputs being devoted to green innovation, starting with a more detailed breakdown of green R&D expenditures (as in Frischtak (2011) for Brazil), and then going beyond R&D investments to measure the broader range of green investments in intangible capital, including other expenditures on innovative property (such as green architectural and engineering designs), on green-related software and databases, and on green-related economic competencies (such as branding, employee training, and organizational improvements).<sup>50</sup> Regarding policy, almost all evidence on the impact of policies to-date is from high-income countries. And even for high-income countries, the evidence on green innovation policy impact is scant. Not only is there not a lot of good data on angel investing in high-income countries, but there are no cost benefit analyses on angel tax credits or other policies to promote angel investors. Nor is there an impact evaluation study on what Chinese banks have been doing to support their green energy producers. Unfortunately, very few policies have been analyzed to figure out where the bang for the buck is. So there is an urgent need for well-designed impact evaluations of specific policy interventions. Both experimental evaluation with randomized controlled trials and quasi-experimental evaluation of existing interventions are needed, particularly regarding the effectiveness of different policies to promote firm-level absorption of existing green technologies.<sup>51</sup>

> 50 See Dutz et al. (2011) for an application of measuring intangible capital to Brazil.

> 51 An impact evaluation (IE) of a program or policy seeks to quantitatively measure the impact of the program on specific outcomes of interest, and is distinct from monitoring or evaluation of program activities. Since beneficiary outcomes are affected by a host of factors (besides the program being evaluated), identification of the causal link between program and outcomes is the key challenge for IE. IE tackles this problem by comparing program beneficiaries (the  ̳treatment‘ group) to a group of non-beneficiaries (the  ̳control‘). The control group is intended to

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