In order to make renewable energy technology deployment strategies politically acceptable, many countries are linking them to other socio-economic goals. A controversial industrial policy tool that is increasingly popular is the use of local content requirements (LCRs). These regulate the extent to which certain projects must use local products and are often justified on the basis of supporting local employment and private sector development. The LCR debate has centred on the rights and wrongs of protecting infant industry, with little progress being made in finding common ground. This article aims to move beyond this stalemate to understand conditions under which LCRs might be an effective tool for promoting local manufacturing. To do so, an effectiveness framework is applied to LCRs for solar photovoltaics in India's National Solar Mission. The article finds that for LCRs to be effective, they must be (1) limited in duration and incorporate planned evaluation phases, (2) focused on technologies and components for which technical expertise is available and global market entry barriers are manageable, and (3) linked to training and promotion of business linkages and linked to support for other stages of the value chain and wider services integral to success of renewable energy industries.
Policy relevance
It is widely appreciated that governments need to support renewable energy technology deployment in order to mitigate climate change. However, policy makers face increasing pressure to link such support with other socio-economic goals, such as job creation, economic development, and poverty reduction. One such policy support mechanism is the use of local content requirements (LCRs) linked to feed-in tariffs. Policy makers are faced with a difficult choice as manufacturing interest groups lobby for the establishment of protection measures such as LCRs whilst the international trade community led by the World Trade Organization (WTO) seeks to limit their use. This difficulty is amplified by the limited information on the impact of LCRs on job creation and economic development. In this context, this article documents the use of LCRs in India's National Solar Mission and seeks to understand the conditions under which LCRs are an effective policy tool for building a competitive local manufacturing industry. 相似文献
Separation of geochemical anomalies from background are one of the important steps in mineral exploration. The Khooni mineral district (Central Iran) has complex geochemical surface expression due to a complex geological background. This region was chosen as a study area for recognition of the spatial distribution of geochemical elements and separating anomalies from background using stream sediment geochemical data. In the past decades, geochemical anomalies have been identified by means of various methods. Some of these separation methods include: statistical analysis methods, spatial statistical methods and fractal and multi-fractal methods. In this article, two efficient methods, i.e. U-statistics and the fractal concentration-area for separation and detection of anomalous areas of the background were used. The U spatial statistic method is a weighted mean, which considers sampling point positions and their spatial relation in the estimation of anomaly location. Also, fractal and multi-fractal models have also been applied to separate anomalies from background values. In this paper, the concentration–area model (C–A) was suggested to separate the anomaly of background. For this purpose, about 256 stream sediment samples were collected and analyzed. Then anomaly maps of elements were generated based on U spatial statistics and the C-A fractal methods for Au, As and Sb elements. According to obtained results, the U-statistics method performed better than C-A method. Because the comparisons of the known deposits and occurrences against the anomalous area created using thresholds from U-statistics and C-A method show that the spatial U-statistics method hits all of 3 known deposits and occurrences, the C-A fractal method hits 1 and fails 2. In addition, the results showed that these methods with regard to spatial distribution and variability within neighboring samples, in addition to concentration value frequency distributions and correlation coefficients, have more accurate results than the traditional approaches. 相似文献