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We collected and processed GNSS time series of 31 SuGAr and 2 AGNeSS stations following the strategy described in Feng et al.8 using the GPS-Inferred Positioning System and Orbit Analysis Simulation Software (GIPSY-OASIS) version 6.2. GNSS daily time series of 11 IGS stations and 3 Memphis stations were downloaded from the Nevada Geodetic Laboratory (Nevada Bureau of Mines and Geology, University of Nevada; http://geodesy.unr.edu/index.php, last accessed on 28 July 2015). GNSS daily time series are processed in ITRF200834. Over the past two decades a number of large subduction zone earthquakes occurred in Sumatra, including 17 events of moment magnitude M  ≥ 6.5 from 2009 up to the IOE (Extended Data Fig. 1). Based on the approach in ref. 9, we take the following steps to derive postseismic displacements from GNSS time series (Extended Data Figs 2 and 3). (1) We correct the time series for the trends of the postseismic transients of the earthquakes before the IOE. We fit the postseismic trends of the previous earthquakes with a logarithmic function of time. (2) We then calculate the long-term secular, annual and semi-annual variations of the time series before the IOE. (3) We correct the post-IOE time series for the trends obtained in step (2). (4) We fit the corrected post-IOE time series using logarithmic and exponential functions of time , where a and b are constants, t is the time, and τ and τ are characteristic time constants of the logarithmic and exponential terms, respectively. τ and τ are determined for each GNSS station through a grid search method9. (5) We then calculate postseismic displacements between any two time epochs from the fitted postseismic curve (Extended Data Fig. 3). For those stations that were discontinued two or more years after the IOE we calculate the 3-year-postseismic displacements through the extended fitted curve. We exclude the following ten stations that have data gaps or show patterns of postseismic displacements obviously inconsistent with that of their neighbouring stations (Extended Data Fig. 1). (1) CARI, AITB and NIMT have data gaps of more than 10 days before and after the IOE, 28 January to 23 April 2012, 2–26 April 2012 and 14 March to 26 April 2012, respectively. (2) NGNG and SLBU move westward almost perpendicular to the northward motion of neighbouring stations. PRKB moves southward, opposite to its neighbouring stations. (3) Horizontal displacements at PTLO, TLLU and KTET are more than five times larger than that of neighbouring stations within 100 km. The vertical displacement at BSAT is more than ten times larger than that of nearby stations. The inconsistency in the postseismic deformation pattern of the above stations is probably due to local processes and/or the bias in removing the postseismic trends of local earthquakes before the IOE. The signal-to-noise ratio at the two AGNeSS stations TANG and ACEH increased after 2014 owing to local construction activities. Since our postseismic displacements for TANG and ACEH are calculated through curve fitting based mostly on the time series of 2012–2014, we do not exclude these two AGNeSS stations. We evaluate test models through calculating the weighted χ2 misfit: where G and F represent GNSS displacement measurements and model predictions, respectively, i represents the station number, the degrees of freedom d.o.f. = 3 in this work are for the three free model parameters, is the variance of the GNSS observation, and N is the total number of GNSS observations. We use six equally spaced time steps (that is, intervals of 6 months) covering the first three years after the IOE. We calculate the χ2 misfit of the horizontal and vertical components separately. A linear sum of horizontal and vertical displacements produces preferred models that fit the horizontal components well, but provide a poor fit to the vertical component. Using a higher weight (such as 10) on the vertical component worsens the fit to horizontal components. Therefore we calculate the total effect by a combination of the horizontal components and five times the vertical component. The spherical-Earth viscoelastic finite-element model used in this work is based on previous studies of the Chile, Sumatra12, 13, 35, 36, and Cascadia subduction zones1 and has been reported in refs 13 and 14. The model includes an elastic upper plate, an elastic slab, a viscoelastic mantle wedge, a viscoelastic oceanic asthenosphere and upper mantle (Fig. 2). Cooling and plate models37, 38, 39 allow for a lithosphere thickness of 50–80 km of the 50–60-million-year-old Indian Ocean plate near the IOE. We thus assume a uniform lithospheric thickness of 50 km, which is also consistent with shear-wave tomography constraints19 and the depth extent of the coseismic rupture of the IOE6, 7. The shear moduli of the elastic lithosphere and viscoelastic upper mantle are assumed to be 48 GPa and 64 GPa, respectively. The Poisson’s ratio and rock density are assumed to be 0.25 and 3.3 × 103 kg m−3, respectively, for the entire domain. Viscoelastic relaxation of the upper mantle is represented by the bi-viscous Burgers rheology15. On the basis of previous studies13 we assume the viscosity of the mantle wedge to be 3 × 1019 Pa s. The coseismic fault slip of the earthquake derived by Wei et al.6 is used in this work through the split-node method40. Different rupture models6, 7, 16 do not change the fundamental pattern of the predicted co- and postseismic motions at GNSS stations hundreds of kilometres from the rupture area (Extended Data Fig. 4). Except for the top free surface, the other five model boundaries are free in the tangential directions and fixed in the normal direction. Domain boundaries are more than 1,000 km from the rupture zone in the horizontal directions. The bottom of the model is at 660 km depth, approximating the transition zone. The setup of the model boundaries produces negligible numerical artefacts on the deformation of the study area, containing these GNSS stations. We first present explorations of the model space, such as the lithospheric thickness, existence of the slab, and the extent of the oceanic asthenosphere. We examine the contribution of the relaxation in the individual rheological units to the surface deformation. Then we evaluate the potential contributions of afterslip of the fault to the postseismic deformation at GNSS stations. We report the range in three model parameters, the thickness (D ) and viscosity (η ) of the oceanic asthenosphere, and the viscosity of the oceanic upper mantle (η ). Finally we present the temporal change in the postseismic surface deformation in the PM. In the following tests we vary some model parameters and keep other model parameters the same as in the PM, that is, D  = 80 km, η  = 2 × 1018 Pa s, η  = 1020 Pa s, and the viscosity of the mantle wedge η  = 3 × 1019 Pa s (Fig. 2). We present model-predicted postseismic displacements at three years after the IOE. Differential surface deformation is calculated by the results of a test model minus that of the PM. If the oceanic asthenosphere has the same viscosity as the underlying oceanic upper mantle, that is, if we consider models with a homogeneous oceanic upper mantle12, 13, 14, a test model with a viscosity of 1020 Pa s in the oceanic upper mantle predicts only about half of the observed postseismic horizontal displacements and subsidence of about 2 cm in the forearc area, in the first three years (Extended Data Fig. 5a). Lowering the viscosity (for example, by one order of magnitude; see Extended Data Fig. 5b) improves the fit to the horizontal GNSS data. However, the test model still fails to predict the observed uplift in the forearc region. A weak oceanic asthenosphere is required to produce the observed uplift. We test a number of model scenarios in which the oceanic asthenosphere is not allowed to extend along the subducting slab, models without a slab, and models with different lithosphere thicknesses. Varying the lithospheric thickness by a couple of tens of kilometres produces negligible changes in the surface deformation (Extended Data Fig. 6a and b). Without the existence of the slab the model predicts additional landward motion near the trench, seaward motion inland, and uplift in the upper plate (Extended Data Fig. 6c). If we assume that the oceanic asthenosphere terminates at the trench and does not extend to greater depths beneath the slab, the differential surface motions three years after the IOE are up to approximately 5 cm near the trench (Extended Data Fig. 6d). We have constructed test models to study the individual contributions of the rheological units to the surface deformation. We allow viscoelastic relaxation only in one rheological unit using its PM parameter and assume the rest of the domain to be elastic. Although this approach ignores the effects of the viscoelastic flow of other rheological units, it helps to understand the first-order pattern of the deformation that is due to each specific relaxation process. If we allow viscoelastic relaxation only in the oceanic asthenosphere (Extended Data Fig. 7a), the test model VEA produces horizontal displacements up to more than 50 cm three years after the earthquake. The VEA produces postseismic uplift of more than 7 cm in the northern Sumatra forearc region. If we allow viscoelastic relaxation only in the oceanic upper mantle (Extended Data Fig. 7b), the test model VEO produces up to about 3 cm of the horizontal displacements. The magnitude of the vertical motions in the VEO is smaller than in the VEA, and its direction is opposite to that of the VEA. If we allow viscoelastic relaxation only in the mantle wedge (Extended Data Fig. 7c), the test model VEM produces generally landward motion of less than 5 cm and subsidence of less than 2 cm in the forearc area. Tests on the sensitivity of the surface deformation to variations in the viscosity of the rheological units also indicate that the relaxation in the oceanic asthenosphere has a more important role in controlling the viscoelastic postseismic crustal deformation than that of the underlying upper mantle and the mantle wedge above the subducting slab (results not shown). Note that the IOE induces stresses mostly at shallow depths (for example, less than about 400 km). The PM shows that the three-year-postseismic displacements are up to approximately 2 cm at depths of 400 km, and are negligibly small (less than 1 cm) at greater depths (exceeding 500 km) (results not shown). Therefore, viscoelastic postseismic surface deformation is controlled mainly by relaxation processes in the shallow upper mantle. We simulate the afterslip after the IOE using a weak shear zone approach14. In a 2-km-thick shear zone extending down to a depth of 65 km, the maximum depth of the rupture of the IOE6, we assume that the locked region is shaped by the 5-m coseismic contour lines within which no afterslip is allowed. Steady-state viscosity η in areas outside the locked region is assumed to be 5 × 1017 Pa s (ref. 13). If we do not allow viscoelastic relaxation in the upper mantle (afterslip only), the test model AFS produces substantial horizontal displacements mainly in the vicinities of the rupture area (Extended Data Fig. 7d). The vertical deformation in the AFS is similar to that of the VEO, that is, it produces subsidence in the forearc where postseismic uplift has been observed. If we apply the same weak shear zone to study the IOE-induced afterslip of the megathrust, the resultant change in the surface deformation is no more than 0.4 cm in the three years after the IOE because the stresses on the megathrust induced by the IOE over 200 km away are negligibly small (results not shown). If we add the contribution from viscoelastic relaxation in the upper mantle using the PM parameters, that is, the model includes the three processes in Extended Data Fig. 7a and c, this afterslip model of η  = 5 × 1017 Pa s produces horizontal displacements at least 50% larger than that in the PM (Extended Data Fig. 8a). Test models with different viscosities in the shear zone produce similar overestimated horizontal GNSS motion (Extended Data Fig. 8b and c). Overestimated motions at GNSS sites are mostly due to afterslip at shallow depths (≤50 km) (Extended Data Fig. 8d). Earthquake-induced stress at greater depths (>50 km) are much smaller, and thus the stress-driven deep afterslip slightly overestimates midfield motions and predicts little changes in the far field (Extended Data Fig. 8e). An afterslip model with a low η  = 5 × 1017 Pa s and a higher η (such as η  = 1020 Pa s), two orders of magnitude higher than in the PM, produces a better fit to the horizontal GNSS data but worsens the fit to the vertical component (Extended Data Fig. 8f). As afterslip produces subsidence at the northern Sumatra stations, adding its contributions generally increases the model misfits. In the PM the oceanic asthenosphere extends to greater depths with the downgoing slab. We constructed a test model in which the oceanic asthenospheric layer terminates at the trench41. Excluding the subducted asthenosphere results in subsidence of up to about 2 cm and southwest seaward displacements of up to about 5 cm in the forearc (Extended Data Fig. 6d). A much lower viscosity (such as η  = 2 × 1017 Pa s; see Extended Data Fig. 9a) or larger thickness (such as D  = 200 km; see Extended Data Fig. 9b) of the asthenosphere is then required to produce a comparable goodness of fit to the land GNSS data. We assumed a sharp boundary between the lithosphere and the asthenospheric layer and did not include details of the lithosphere–asthenosphere boundary because of the limits of the spatial coverage of the GNSS network. We constructed a test model to study the effect of including a rheological transition between the lithosphere and asthenosphere. In the test model we assume a 20-km-thick transition zone in which the viscosity decreases linearly with depth from 1022 Pa s at the bottom of the lithosphere to the preferred 2 × 1018 Pa s of the asthenosphere. Other model parameters are the same as in the PM. This transition-zone model produces a change of no more than 5 cm in surface displacements in areas within 200 km of the rupture area and approximately zero at the land GNSS stations in the first three years after the IOE (Extended Data Fig. 9c). This test thus indicates that the sharpness of the lithosphere and asthenosphere boundary cannot be resolved by the sparse geodetic observations. Overall the relaxation in the oceanic asthenosphere is the primary process controlling the postseismic surface deformation and is the only process that produces the observed uplift in the northern Sumatra forearc. Surface deformation is much more sensitive to the rheological structure below the oceanic lithosphere than to that on the continental side where most of the GNSS stations are located. These test models thus illustrate that the IOE provides a unique opportunity to constrain the rheological structure of the oceanic upper mantle. We derive the range of the model parameters by selecting those test models fitting the overall pattern of the GNSS data in both horizontal and vertical directions. The test model best fitting the horizontal GNSS data has χ2 = 5.8 and does not predict the observed uplift in northwestern Sumatra forearc (Extended Data Fig. 9d). The test model best fitting the vertical GNSS data has χ2 = 6.96 and overestimates the horizontal data (Extended Data Fig. 9e). We have found that test models with χ2 ≤ 5.3 reproduce the first-order pattern of the GNSS data, that is, misfit of the horizontal components is less than about 20%, and the model predicts more than about 20% of observed uplift at these closest GNSS stations, such as UMLH, LEWK, BNON and BSIM. Test models of χ2 ≤ 5.3 in Fig. 3c thus give the ranges as D  = 30–200 km, η  = (0.5–10) × 1018 Pa s, and η  = (0.5–100) × 1020 Pa s. We examine the evolution of the spatial pattern of the predicted viscoelastic postseismic surface deformation in the PM following the IOE (Extended Data Figs 10). The peak horizontal displacements in the upper plate increase from around 10 cm one year after the IOE to more than 50 cm ten years after the IOE (Extended Data Figs 10a–c). Horizontal displacements increase steadily over time and exhibit only small changes in orientation (Extended Data Fig. 10d, e).The vertical surface displacements are generally divided into four uplift–subsidence quadrants, a common pattern of the postseismic deformation following a strike–slip earthquake. An interesting feature is the change in the direction of the vertical displacement in the northeastern quadrant in the continental upper plate (Extended Data Fig. 10a–c, f). In this quadrant the vertical motion one year after the IOE is uplift near the rupture area and subsidence farther inland (Extended Data Fig. 10a, f). The area of the subsidence region shrinks with time, and the uplift region expands.


Singh S.,CARI | Singh D.R.,CARI | Banu S.,Horticulture Biochemistry and Biotechnology Laboratory | Salim K.M.,Horticulture Biochemistry and Biotechnology Laboratory
Proceedings of the National Academy of Sciences India Section B - Biological Sciences | Year: 2013

Some of the plant derived bioactives are called as 'natural antioxidants' for their role in protecting the cells from injurious effect of reactive oxygen species. The study investigated the concentration of natural bioactives and antioxidant capacity in Eryngium foetidum L. leaves. The antioxidant activity was determined in leaf extracts prepared in five solvents (methanol, acetone, petroleum ether, chloroform and water). The concentration of bioactive compounds (polyphenol, tannin, anthocyanin, flavonoids, carotenoids and ascorbic acid) varied in the extracts prepared with different solvents. The highest recovery of these bioactive compounds was observed with acetone and methanol. The contents of anti-nutritional factors, namely saponin, nitrate, phytate and oxalate content were also estimated in the leaf extracts. HPLC analysis of methanol leaf extract led to detection of several carotenoids (lutein, zeaxanthin, β-cryptoxanthin, β-carotene, chlorophyll-a, chlorophyll-b and pheophytin-b), phenolics (gallic acid, protocatechuic acid, syringic acid, p-coumaric acid, ferulic acid and sinapic acid) and anthroquinones (norlichexanthone, telochistin, secalonic acid D, citreorosein, emodin and parietin). Present study has revealed antioxidant potential of E. foetidum for possible use by food and pharmaceutical industry. © 2012 The National Academy of Sciences, India.


Island ecosystem is unique but with a great diversity. Marine resource potential of Andaman and Nicobar Islands (ANI) is underutilized. The sensitive ecosystems of corals and Mangroves are facing threats as a result of changing climate. Potential fishery resources need to be exploited in sustainable manner for income and employment generation of islanders. Primary data on resources of Bay Islands are collected resorting to standard survey methods and secondary data are used as supporting data for analyzing the trend and potential of fisheries in ANI. The paper is depicting in details the major marine resources and their status in Bay Islands and approaches for their sustainable exploitation and conservation.


Biswas A.K.,Indian Veterinary Research Institute | Kondaiah N.,Indian Veterinary Research Institute | Ram Anjaneyulu A.S.,Indian Veterinary Research Institute | Rao G.S.,Indian Veterinary Research Institute | Singh R.P.,CARI
Analytical Methods | Year: 2010

A simple and sensitive liquid chromatographic (LC) method was developed for determination of carbaryl residue in buffalo meat samples. This method is based on a solid-phase extraction technique followed by high-performance liquid chromatography (HPLC)-photo-diode-array (PDA) detection. Meat samples (0.5 g) were deproteinized by adding acetonitrile followed by centrifugation and filtration. The analyte was separated on a reverse-phase (RP-C18) column using isocratic elution. Acetonitrile along with water appears to be an excellent extractant as recovery of the analyte in spiked sample at maximum residue level (MRL) was 98.5%, with coefficient of variation (CV) of 4.97%. The limit of detection (LOD) and limit of quantification (LOQ) of the method was 0.015 and 0.03 μg g-1, respectively. The linearity of the carbaryl was 0.9992. Excellent method repeatability and reproducibility were also observed by intra- and inter-day assay precision. For robustness, the method was employed to analyze 122 buffalo meat samples, and intensities for the insecticide were found to be unaffected by the sample matrices interference. © 2010 The Royal Society of Chemistry.


Swarnam T.P.,CARI | Velmurugan A.,CARI
Environmental Monitoring and Assessment | Year: 2013

Vegetable samples of brinjal, okra, green chilli, crucifers, and cucurbits collected from farmers' field were tested for the presence of organochlorine (OC), organophosphorus (OP), and synthetic pyrethroid (SP) compounds using a gas chromatograph equipped with electron capture and flame thermionic detectors. Of the samples tested, 34.0 % were found to have pesticide residues. Among the OC compounds, α-endosulfan, β-endosulfan, and endosulfan sulfate were detected in 14.5 % of the samples with residues. These were taken from crucifer, okra, green chilli, and cucurbit samples. SP compound residues, such as α-cypermethrin, fenvalerate I, fluvalinate I, deltamethrin, and λ-cyhalothrin were detected in 32 % of the samples with residues. OP compound residues such as chlorpyrifos, profenofos, monocrotophos, triazophos, ethion, dimethoate, and acephate were found in 54 % of the samples with residues, which were taken from all vegetable samples. Of the positive samples, 15.3 % were found to contain residues exceeding the prescribed maximum residue limit. The average pesticide residue content across all the vegetable samples was 0.108 ppm, with values ranging from 0.008 to 2.099 ppm. Multiple residues of more than one compound were detected in 34.1 % of samples containing residues. © 2012 Springer Science+Business Media Dordrecht.


Jairath G.,Guru Angad Dev Veterinary and Animal Sciences University | Chatli M.K.,Guru Angad Dev Veterinary and Animal Sciences University | Sahoo J.,Guru Angad Dev Veterinary and Animal Sciences University | Biswas A.K.,CARI
Journal of Food Science and Technology | Year: 2015

The storage stability of enrobed goat meat bites (EGMB) incorporated with 3 % crude aloe vera (AV) gel was evaluated under aerobic (T-1; unenrobed control product, T-2; enrobed AV treated product) and modified atmospheric packaging (MAP, 50:50, CO2 and N2) (T-3; unenrobed control product, T-4; enrobed AV treated product) at 4 ± 1 °C for 42 days on the basis of physico-chemical, microbiological and sensory attributes. The pH value was higher, whereas water activity (aW) was lower in enrobed and MAP product. Thiobarbituric acid reacting substances (TBARS) and free fatty acid (FFA) values were significantly (P < 0.05) lower in MAP packaged (T-3) and AV treated products (T-2 and T-4) products than aerobic packaged (T-1), however it followed an increasing trend in all the products throughout storage. Instrumental colour and textural profile attributes were better maintained in MAP products than others. The sensory panellists graded T-4 ‘good to very good’ even on Day 42, whereas T-1 was acceptable only up to 28 days. Standard Plate Count (SPC) was significantly (P < 0.05) lower in MAP products than aerobic packaged products. Results concluded that EGMB treated with AV gel can be successfully stored more than 42 days under MAP conditions without affecting its physico-chemical, textural, microbiological and sensory attributes. © 2014, Association of Food Scientists & Technologists (India).


PubMed | Guru Angad Dev Veterinary and Animal Sciences University and CARI
Type: Journal Article | Journal: Journal of food science and technology | Year: 2015

The storage stability of enrobed goat meat bites (EGMB) incorporated with 3% crude aloe vera (AV) gel was evaluated under aerobic (T-1; unenrobed control product, T-2; enrobed AV treated product) and modified atmospheric packaging (MAP, 50:50, CO2 and N2) (T-3; unenrobed control product, T-4; enrobed AV treated product) at 41C for 42days on the basis of physico-chemical, microbiological and sensory attributes. The pH value was higher, whereas water activity (aW) was lower in enrobed and MAP product. Thiobarbituric acid reacting substances (TBARS) and free fatty acid (FFA) values were significantly (P<0.05) lower in MAP packaged (T-3) and AV treated products (T-2 and T-4) products than aerobic packaged (T-1), however it followed an increasing trend in all the products throughout storage. Instrumental colour and textural profile attributes were better maintained in MAP products than others. The sensory panellists graded T-4 good to very good even on Day 42, whereas T-1 was acceptable only up to 28days. Standard Plate Count (SPC) was significantly (P<0.05) lower in MAP products than aerobic packaged products. Results concluded that EGMB treated with AV gel can be successfully stored more than 42days under MAP conditions without affecting its physico-chemical, textural, microbiological and sensory attributes.


PubMed | Guru Angad Dev Veterinary and Animal Sciences University and CARI
Type: Journal Article | Journal: Journal of food science and technology | Year: 2015

A three factor Box-Behnken design of response surface methodology was employed to optimize spent hen meat level (600-700gkg(-1)), oil level (25-75gkg(-1)) and cooking time (3-5min) for development of ready-to-eat chicken meat caruncles on the basis of sensory attributes - colour/appearance, flavour, crispiness, after-taste, meat flavour intensity and overall acceptability. The analysis of variance showed that meat and cooking time interaction showed significant effect (p<0.01; p<0.05; p<0.1) on colour/appearance and crispiness of chicken meat caruncles. Quadratically meat level showed significantly higher effect (p<0.01; p<0.05; p<0.1) on crispiness; and oil level and cooking time (p<0.05; p<0.1) on after-taste of chicken meat caruncles. Linearly meat level showed significantly higher (p<0.05; p<0.1) effect on colour/appearance, after-taste, meat flavour intensity and overall acceptability of chicken meat caruncles. The optimized conditions were: 650gkg(-1) meat level, 50gkg(-1) oil level and cooking time as 4min. Among all sensory parameters, crispiness is one of the most important sensory parameters for meat snacks, which was highest (6.68) at the optimized conditions in the final product. The other sensory parameters ranged from 6.33 to 6.68 on an eight point scale. Box-Behnken design of RSM performed well in the optimization process of development of chicken meat caruncles to produce product with very high degree of acceptability. 650gkg(-1) of spent hen meat level produced the most acceptable product in terms of sensory profile.


News Article | March 2, 2016
Site: www.theenergycollective.com

China is Africa’s largest trading partner, providing demand for the continent’s energy and minerals, and its direct investments in the continent are also on the rise. When Chinese Premier Li Keqiang visited the African Union in 2014, he announced that China would raise its direct investment in the continent to $100 billion by 2020, mostly in infrastructure development. While Chinese companies have been involved in Africa’s energy industry for years, particularly in hydro-electricity and fossil fuel extraction, the rise of China’s involvement in the continent’s renewable energy sector is relatively recent and an area ripe for further research. New research, which aims to better understand China’s investment in South Africa’s renewable energy sector, will help provide some answers. Lucy Baker, from the Science Policy Research Unit at the University of Sussex and Wei Shen, from the Institute of Development Studies, have received a fellowship to better understand the drivers and obstacles to the expansion of Chinese renewable energy activities in South Africa. There has been growing Chinese involvement in the wind and solar PV industries under South Africa’s renewable energy independent power producers’ procurement programme (RE IPPPP), a competitive bidding system for renewable energy generation by independent power producers. The research will focus on how Chinese companies and investors are involved in South Africa’s renewable energy, including their engagement in project development, investment, technology supply and manufacturing. The research will also consider implications for Chinese involvement in emerging renewable energy development elsewhere in the region and for other middle-income economies with a significant renewable energy programme under development, such as Argentina, India and Brazil. Their findings will be shared as a policy brief to be published by SAIS-CARI. The SAIS-CARI Fellowship they have been awarded allows researchers, policy-makers, or journalists to do field research on an under-explored policy issue related to China’s African engagement.


News Article | March 2, 2016
Site: www.theenergycollective.com

China is Africa’s largest trading partner, providing demand for the continent’s energy and minerals, and its direct investments in the continent are also on the rise. When Chinese Premier Li Keqiang visited the African Union in 2014, he announced that China would raise its direct investment in the continent to $100 billion by 2020, mostly in infrastructure development. While Chinese companies have been involved in Africa’s energy industry for years, particularly in hydro-electricity and fossil fuel extraction, the rise of China’s involvement in the continent’s renewable energy sector is relatively recent and an area ripe for further research. New research, which aims to better understand China’s investment in South Africa’s renewable energy sector, will help provide some answers. Lucy Baker, from the Science Policy Research Unit at the University of Sussex and Wei Shen, from the Institute of Development Studies, have received a fellowship to better understand the drivers and obstacles to the expansion of Chinese renewable energy activities in South Africa. There has been growing Chinese involvement in the wind and solar PV industries under South Africa’s renewable energy independent power producers’ procurement programme (RE IPPPP), a competitive bidding system for renewable energy generation by independent power producers. The research will focus on how Chinese companies and investors are involved in South Africa’s renewable energy, including their engagement in project development, investment, technology supply and manufacturing. The research will also consider implications for Chinese involvement in emerging renewable energy development elsewhere in the region and for other middle-income economies with a significant renewable energy programme under development, such as Argentina, India and Brazil. Their findings will be shared as a policy brief to be published by SAIS-CARI. The SAIS-CARI Fellowship they have been awarded allows researchers, policy-makers, or journalists to do field research on an under-explored policy issue related to China’s African engagement.

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