VivoPower establishes UAE subsidiary at DMCC, the world’s premier free trade zone in Dubai
UAE has announced its US$200bn Net Zero 2050 Strategic Initiative
VivoPower will service Middle East, Africa, and Indian subcontinent markets from Dubai hub
VivoPower International PLC is pleased to announce that it has established a subsidiary in the United Arab Emirates (UAE) as it expands its capabilities to service the Middle East and surrounding markets. The company’s new subsidiary entity is located at the world’s premier free trade zone, the Dubai Multi Commodities Centre (DMCC). Dubai’s DMCC has won the Financial Times’ fDi Magazine’s Global Free Trade Zone of the Year award for a seventh consecutive year. The DMCC is the Government of Dubai Authority on commodities trade and enterprise.
Kevin Chin, Executive Chairman and CEO of VivoPower, said: “The Middle East is the largest market in the world for Toyota Land Cruisers, and we are very pleased to establish a presence here. Increasing our physical footprint allows us to expand our capabilities to effectively service this strategically important market. Furthermore, the UAE Government has just announced its landmark Net Zero 2050 Strategic Initiative with Dh 600 billion (US$200 billion) to be invested in clean and renewable energy solutions over the next 30 years. Aside from covering the Middle East, Dubai will serve as a critical logistical hub to service the African and Indian subcontinent markets. We are grateful to our partners and other counterparts in Dubai who have seamlessly helped us set up our operations in a business-friendly manner while safeguarding essential health and safety protocols. We look forward to servicing this very important region with our sustainable energy solutions and contributing to the Net Zero 2050 Strategic Initiative.” To read our full press release, and to keep up with all VivoPower’s releases, visit our Press Releases page.
By Dr. Nick James, Data Scientist, Arowana
This white paper studies trends in the propagation of hydrogen plants and their respective capacity over the past two decades. We introduce a number of mathematical frameworks to reveal new insights into the evolution of hydrogen energy production across a number of geographic regions. We use data from the International Energy Agency.
Hydrogen has great potential as an alternative fuel source and may play a role in the world’s coordinated attempt to reach net-zero emissions during this century. Hydrogen could be used for numerous purposes, including industry, transport, heating and energy production. Indeed by mass, hydrogen contains more than twice the energy potential of natural gas. However, hydrogen in its pure form does not exist naturally on earth. It must be synthesised via a variety of different procedures.
The production of hydrogen is classified by colours according to its mode of preparation. “Green hydrogen’’ refers to production techniques that do not generate any greenhouse gas (GHG) emissions. This is of course the ideal method of production to forward the goal of an alternative energy source that does not contribute further to global warming. Typically, green hydrogen plants use renewable sources of energy (such as solar) to extract hydrogen via the electrolysis of water. This is clearly a desirable process, however, the energy production capacity of green plants has been quite limited. On the other hand, black, brown, and grey hydrogen refers to production techniques that use black coal, brown coal, and natural gas respectively, generating harmful gases including carbon dioxide and monoxide. Blue hydrogen is defined as the generation using natural gas, followed by carbon capture and storage (CSS). This is not a truly zero-emissions process, as not all generated GHGs can be captured.
There is significant variability in a country’s level of sophistication with regards to alternative and clean energy. Many European countries such as Germany have been able to reduce carbon dioxide emissions over the past 50 years, largely driven by their willingness to drive the adoption of new technologies, often with the significant initial cost. Less willing to wear the immediate economic consequences, many developing countries, as well as the United States and Australia, have been more hesitant to commit to green and alternative energy sources. Instead, they continue to utilise energy sources that have already reached economies of scale.
This paper investigates trends in the rollout and prevalence of hydrogen plants by both technology and location. We study the changing propagation and capacity of hydrogen plants over time, with a particular interest in the increasing potential of green hydrogen plants. We also investigate differences in these rollouts on a geographic basis. Our main finding is promising: an exponential increase in the capacity of green plants over time, and a dramatic closing between the capacity of green and non-green plants.
Our data comes from the International Energy Agency, and consists of plants built-in (or projected to be built-in) 2000–2028, a period of T=29 years. Each plant is classified according to one of five underlying technologies: four different types of water electrolysis, which are all green plants, and ``fossil’’, indicating the use of fossil fuels. By aggregating the types of electrolysis, the plants are essentially classified as green or non-green (``fossil’’). For each plant, the location is also recorded, either specified by the country of location or continent. As such, all our analysis proceeds on a continent-by-continent basis, in which we record and analyse only the continent of location. We divide the plants into continental groups as follows: North America, South America, Europe, Oceania, East Asia (China and Japan) and Other Asia (mostly consisting of Indian plants).
In Figure 1, we display all the plants with a known energy capacity in our dataset. Displaying the logarithm of the capacity against the year of construction, we see an approximate linear trend between the log of capacity and the year. This suggests an exponential growth in capacity vs time. Earlier on, we see that fossil (ie non-green) plants are several orders of magnitude greater in capacity than green plants, but this difference reduces dramatically over time. To further explore these findings we implement two linear regressions:
We encode six continents as Europe, North America, South America, East Asia (Japan and China), Other Asia (predominantly India), Oceania, with Europe as the default categorical variable. We also consider five technologies: ALK, PEM, SOEC, Unknown PtX and Fossil, with ALK as our default variable. For model 1, the adjusted R² is 0.17, while for model 2, the adjusted R² is 0.65. This strongly suggests a better fit where capacity is predicted and understood to increase exponentially over time, confirming the qualitative observations in Figure 1.
We include additional details on model 2 in Table 2. Relative to Europe as a baseline, South America has a significantly greater capacity p=0.042, while PEM and SOEC have less capacity than ALK. This could be of potential interest to those interested in owning and operating green hydrogen plants. In particular, one could consider the profitability implications when contrasting the varying costs of electrolysis utilising PEM, SOEC and ALK, and a candidate plant’s respective output.
In this section, we study the geographic propagation of hydrogen plants around the world. We use the same n=6 continent groups as in previous sections. We wish to investigate the changing geographic spread of hydrogen plants and their energy production capacity with time. For that purpose, we convert these time series into rolling distributions.
In Figure 2a, we plot the time-varying geographic variance of grouped 5-year distributions. That is, this captures the geographic spread of new plants throughout a rolling 5-year period. As elsewhere in the manuscript, we are consistently interested in the analysis of green vs non-green plants; in this case, due to the comparatively low number of fossil plants, the plot is near identical if we consider all new plants or just all green plants.
Both figures 2a and 2b exhibit the same trajectory between 2004–2024, where geodesic variance increases until 2008, decreases until 2016 and then increases until 2024. Both figures start with an initially low geodesic variance in 2005, which is likely explained by Europe’s dominance in hydrogen plant propagation. During the initial increase, we see several plants appear throughout Asia, which leads to an increase in the variance. Between 2010–2015, the geodesic variance declines, which is likely due to East Asia’s levelling off in hydrogen plant propagation. In 2015–2016, East Asia experiences a significant boost in hydrogen plant commencement. This is largely due to China, whose first plant appears in 2017. Since then, China has accounted for numerous other hydrogen plants. We see a similar pattern for our two collections, all hydrogen plants and green hydrogen plants exclusively. This is predominantly due to the green hydrogen plants accounting for the vast majority of all hydrogen plants. The sharp rise in geodesic variance beyond 2016, may indicate the increased awareness of clean energy and decarbonisation.
To elucidate the changes in plants by continent, we plot the cumulative number of plants and fit linear trends to them in Figure 3. We highlight the results of Europe, North America, East Asia and other Asia in Figures 3a, 3b, 3c and 3d respectively. Europe, North America and Other Asia all display a relatively consistent linear trend throughout the entire period of analysis.
However, East Asia’s cumulative number of plants is not modelled well by a linear fit. The cumulative number of plants is perhaps best modelled by a hyperbolic function, given the relatively constant level between 2000–2015, followed by rapid growth between 2015–2020, and another constant period during the early-mid 2020s. These patterns, which demonstrate the consistency of hydrogen plant propagation over time, may indicate the evolution of each continent’s interest in decarbonisation and hydrogen production
Next, we explore the distance correlation between North America and Europe in Figure 4. Distance correlation is not to be confused with the better known, more widely used Pearson correlation. Distance correlation captures linear and nonlinear associations between two random variables, while Pearson correlation can only detect linear relationships.
Early on in our analysis window, the distance correlation is low — which is predominantly due to the sparsity of data. Beyond that, our time-varying distance correlation exhibits a local minimum in 2013. During this time, Europe and North America display profound differences in their concavity behaviours in new plants. During this period, Europe’s cumulative plants display a concave up shape, while North American cumulative plants display a concave down shape. Beyond this point, both North America and Europe exhibit relatively consistent linear increases, which is reflected in the high distance correlation between these two regions’ cumulative plants.
In Figures 5a and 5b, we plot the proportion of fossil plants and capacity, respectively, with time. We see that initially, there are no fossil plants, but as soon as the first appears in each continent, it dominates close to 100% of that continent’s hydrogen energy capacity. However, this dips to more reasonable proportions as we approach the end of the period of analysis.
In Figure 5c, we display the time-varying matrix norm over our period. This total inconsistency is zero until 2011 due to either the complete lack of fossil plants (hence no inconsistency between the production vs the number of fossil vs green plants) and then only Europe having fossil plants (so the adjacency matrices have a block diagonal structure):
Then in 2011, we see North America generate a fossil project which takes up 99% capacity as fossil and 20% of plants are fossil. This is the first introduction of inconsistency between the continents in terms of their relationships between their proportions of fossil and green number of plants vs variance. Subsequently, when other continents start to roll out fossil-related projects, they account for most capacity and relatively small # plants, promoting further inconsistency in # fossil projects and their capacity. In 2016, both Other Asia and East Asia establish their first fossil plants.
Please note that these findings are highly dependent on the dataset we have used. Future studies on more up-to-date, and possibly richer datasets, could reveal further insights related to hydrogen plant propagation and their capacity. As always, please feel free to email me at [email protected] if you have any questions related to the white paper.
In this white paper, we introduce new frameworks to study the spatio-temporal patterns in carbon dioxide emissions, demographic trends, and economic patterns across 50 countries over the past five decades. Our analysis is divided into three sections.
First, we do a decade-by-decade study of carbon dioxide emissions trajectories, demonstrating notable changes in cluster structures for each time period. This highlight shifts between country emissions profiles over time.
Next, we introduce a new method to classify countries into one of three characteristic emissions classes, where countries are categorised according to their number of linear components. Here, we highlight that most countries are best represented by a piecewise linear model with one change point. This suggests that most countries experienced two periods of characteristic emissions during our analysis window.
Finally, we use GDP, population, and carbon dioxide data, and apply dimensionality reduction and clustering to group countries based on similarity in their real and carbon economies. This technique is a new way of viewing similarities between countries, capturing economic and emissions data over 50 years.
Decade-by-decade emissions trajectories
In this section, we investigate country emissions trends on a decade-by-decade basis, exploring the evolutionary structure of country behaviours. Both the number of clusters and cluster constituents are dynamic, with both varying as we proceed forward in time.
First, we take the period from 1970–1979, where we observe a predominant cluster consisting of three sub-clusters, and a small collection of outlier countries (primarily displayed as separate sub-clusters within the predominant cluster). The dendrogram corresponding to this period is shown in Figure 1a. All sub-clusters within the predominant cluster produce increasing trends of carbon dioxide emissions over the decade. The first sub-cluster consists of countries such as Russia and the Ukraine. These countries are characterised by huge growth and accelerating emissions trends. The second sub-cluster, consisting of Italy, Canada, and Argentina, exhibit moderate growth in carbon dioxide emissions. The final cluster which includes countries such as Spain and Brazil also display relatively steady growth behaviours. Countries such as Vietnam and Kuwait exhibit declining carbon dioxide emissions, which is anomalous with respect to the rest of the collection.
Next, we turn to 1980–1989, shown in Figure 1b. This period produces a dendrogram consisting of three distinct clusters. The first cluster contains countries such as India and China, which display continued growth throughout the period. The second cluster consists of a variety of countries but specifically displays pronounced similarities between the Eastern European nations of Kazakhstan, Russia, and Ukraine. These countries all experienced huge growth in emissions, peaking at around 1990. The final cluster consists of France, Belgium, and Nigeria. These countries display erratic emissions behaviours, with all trajectories displaying limited trends and substantial volatility.
In the 1990–1999 period shown in Figure 1c, countries form one primary cluster, with a small collection of outlier countries in a separate, significantly smaller cluster. The primary cluster consists of countries that displayed consistent growth to varying extents over the prior decade. The small outlier cluster consisting of countries such as Kazakhstan, Russia, and Ukraine, all experienced precipitous drops in their emissions at the beginning of the period—and this behaviour continued throughout the remainder of the time period. The latent phenomenon that the cluster formation captures here is the fall of the Iron Curtain in Eastern Europe.
The 2000–2009 period produces one primary trajectory cluster consisting of two similarly-sized sub-clusters, and a collection of outlier countries. The hierarchical clustering results are displayed in Figure 1d. The clear bifurcation in the large cluster is indicative of contrasting trends in emissions behaviours between various countries. The first sub-cluster consists of countries such as the Netherlands, Italy, Germany, the US, Canada, Japan, and Belgium. Most countries within this sub-cluster are more developed and have taken a stronger stance in introducing policies to reduce emissions. Such countries exhibit either flat or declining emissions trajectories throughout the decade. The second sub-cluster consisting of countries such as India, Iran, and Turkey, produce sustained growth in emissions during this period. The significantly smaller second cluster consists of Oman, China, Vietnam, and Qatar, that produce emissions profiles like that of the second sub-cluster.
Finally, we turn to the most recent decade of 2010–2019, as shown in Figure 1e. This period produces three characteristic classes of emissions trajectories. The first cluster consists of countries with lower HDI (human development index) levels: Vietnam, Bangladesh, the Philippines, Iraq, and Pakistan. HDI is a summary metric representing average achievement in various areas of human life. These countries produce emissions trajectories that increase significantly throughout the period.
The second cluster consists of Venezuela, Ukraine, the UK, Italy, and UAE. These countries produce mostly declining trajectories which may represent a greater collective focus on reducing carbon dioxide emissions at the national level. The final cluster consists of two primary sub-clusters. The first contains countries such as Japan, the Netherlands, and Belgium. These countries are primarily characterised by erratic emissions output, with a declining trend overall. The second sub-cluster consists of countries such as China, Thailand, and Chile, that produce a positive trend over the entire decade, with the rate of increase having slowed, and in some cases, overall emissions have begun to trend downward.
Characteristic emissions classes
In this section, we introduce a new method to classify countries based on their emissions trends over time. Having noticed that many countries exhibit a piecewise linear trend in their CO2 emissions, we introduce a new framework to determine the most appropriate model for each country.
We assume that each country’s emissions behaviours over time is best represented by one of the three following models:
Our algorithmic procedure optimises with respect to the number and placement of (up to two) change points, such that our function’s average R² is maximised. We compute average R², taking an arithmetic average of all segments’ R². Given our preference for a simpler model in the case where a more complex one accounts for a similar level (or marginally more) explanatory variance, we introduce a slight penalty for model complexity.
The most frequently occurring model is M_1, followed by M_2, and then M_0. That is, in most cases, carbon dioxide emissions are best modelled with the existence of either one or two change points.
In Figure 2, we display two representative countries for each model. In some cases, such as Algeria in figure 2a and Germany 2b, there is one persistent trend over the past 50 years. For Algeria, there is a consistently positive trend in emissions, while in Germany, emissions are best modelled by a linear decline over the past 50 years.
In the case of Egypt (figure 2c) and Morocco (figure 2d), both countries produce a more strongly positive linear slope beyond the late 1980s and early 1990s. Thus, these phenomena are best modelled with one change point. In the case of the Netherlands and India, as seen in Figures 2e and 2f respectively, both emissions patterns require two change points to model the dynamics.
For the Netherlands, there are obvious discontinuities and abrupt changes in total emissions, seen in the early 1980s and early 2000s. While in India, the early 1980s and mid-2000s display obvious increases in the slope of carbon dioxide emissions.
Identifying similarity in real and carbon economies
Here we introduce a new method to identify countries that share similar economic, demographic, and emissions histories. First, we gather GDP, population, and CO2 emissions data over the past 50 years, and generate distance matrices between all countries for each respective measurement. We then generate an aggregate distance matrix, where each metric is scaled by a constant, such that we normalise for the scale of each metric in our aggregate distance. We then apply multi-dimensional scaling to the distance matrix, projecting the matrix into lower-dimensional data space, and apply K-means clustering to the resulting projection. The number of clusters, K, is determined by optimising the silhouette score. For the sake of interpretability, we accompany these K-means clustering results with a hierarchical clustering dendrogram.
When we apply this analysis to all 50 countries, the USA exhibits highly anomalous behaviour. In fact, we must sWhen we apply this analysis to all 50 countries, the US exhibits highly anomalous behaviour. In fact, we must sequentially remove the US, China, India, Russia, and Japan to identify the general structures without these outlier countries. In the subsequent analysis, we exclude these five and study the structural patterns among the remaining nations.
First, we explore the similarity between countries with respect to each metric individually. Figure 3 displays the three distance matrices for GDP, population, and C02 emissions. In figure 3a, one can see that Germany, France, and the UK are the most anomalous countries with respect to GDP based on their significant economic output over the past 50 years. In figure 3b, we see the distance between country populations, where Indonesia, Brazil, and Bangladesh exhibit the greatest dissimilarity to the rest of the collection. In figure 3c, Germany exhibits the greatest dissimilarity to the remainder of the group.
For the sake of exposition, we present the hierarchical clustering results of our distance matrix capturing similarity in countries’ real and carbon economies. Figure 4 highlights the existence of one prominent cluster, a small sub-cluster, and an outlier.
The primary cluster consists of three sub-clusters, the first of which includes countries such as Chile, Iraq, Vietnam, Romania, Bangladesh, etc. Most countries in this cluster are characterised by developing economies, growth in population, and significant growth in carbon dioxide emissions over time.
The second sub-cluster consists of countries such as the Netherlands, Argentina, Nigeria, Taiwan, etc. These countries typically displayed consistent growth in GDP and population, and lower levels of carbon dioxide emissions over time. The final sub-cluster consists of Spain, South Korea, Indonesia, Mexico, Canada, and Turkey. Most of these countries displayed moderate growth in GDP and population, and growth in carbon dioxide emissions, exhibiting reasonable variability over time.
Many of these countries experienced a flattening in emissions over the final 5–10 years of the analysis window. The second cluster includes the UK, France, Italy, and Brazil. These countries are characterised by increasing emissions trajectories until the later parts of the analysis window, where a flattening or decline in emissions occurs. Germany is identified as an outlier, which is due to its steady decline in emissions over time and the strong GDP growth over the entire analysis window.
This analysis may probe further interesting studies. First, one could explore the key industries and sectors of the economy that drive emissions. This may vary significantly between countries, or perhaps, select industries drive most of the emissions.
Next, one could explicitly study the correlation between population growth and emissions and identify countries that have done the best job in controlling emissions levels relative to their population growth. Figure 5 displays the emissions profile of five major countries: China, the US, India, Russia, and Japan. Basic visual inspection confirms the pronounced similarity between China and India’s emissions profiles, while the US and Japan share broadly similar emissions histories. Russia’s sharp decline in emissions in the early 1990s corresponds to the fall of the Iron Curtain. One could speculate that these emissions are highly related to GDP—and studying this relationship could explicitly provide salient insights.
Conclusion
We apply recently introduced and new methods in spatio-temporal data analysis to identify structural similarity and evolutionary patterns in emissions behaviours over time. First, we demonstrate pronounced heterogeneity in cluster number, size, and constituency among emissions trajectories on a decade-by-decade basis. This section highlights the dynamic nature of this problem and the need for constant monitoring of country behaviours. In the second section of this white paper, we demonstrate that most countries are well-modelled by a piecewise linear data generating process. The consistency of this finding is surprising and would be interesting to monitor moving forward. Should countries more actively police their emissions, one could expect to see further propagation of change points in the near term.
Finally, we introduce a framework to group countries based on their real and carbon economies. Our methodology captures GDP, population, and emissions data over the past 50 years, and includes dimensionality reduction and clustering. This framework could be applied to other problems, or one could generalise some of the questions explored in this paper using different economic and demographic metrics.
VivoPower International PLC is pleased to announce that its wholly-owned subsidiary in Australia, J.A. Martin Electrical Pty Limited (J.A. Martin) has been awarded a contract to complete all electrical works for the 119 MW-DC Hillston Solar Farm in the Riverina region of southwestern New South Wales.
Construction has already commenced with the project to be the third Australian solar farm completed by J.A. Martin in partnership with lead contractor GRS. This brings J.A. Martin’s total of completed and contracted solar farms to over 450 MW-DC.
Once energised, the Hillston Solar Farm will generate approximately 235,000 MWh of clean energy per year, enough to power nearly 54,000 homes and avoid over 161,000 tonnes of carbon dioxide emissions annually. The project’s construction will create about 160 local jobs.
Phil Lowbridge, General Manager of J.A. Martin, said: “J.A. Martin is excited to have the opportunity to once again work with GRS to construct another major solar farm. We look forward to completing another successful project and continuing to help power the growth of renewable energy in New South Wales and across Australia.”
To read our full press release, and to keep up with all VivoPower’s releases, visit VivoPower's Press Releases page.
VivoPower International PLC is pleased to announce that the company has been recognised as one of the global winners of the 2021 Turnaround Management Association (TMA) Transaction of the Year Awards. The honour is in recognition of the hyperturnaround of VivoPower in 2020 and is for the small companies (sub-US$50m revenue) category.
Since 1993, TMA has honoured excellence through its annual awards program, which recognises the most successful turnarounds and impactful transactions internationally. Awardees are chosen through a rigorous peer-review process by the volunteer TMA Awards Committee. The process includes extensive diligence of each nominated case, with the judges reviewing all components of each entry and examining well-defined, measurable outcomes.
Kevin Chin, Executive Chairman and CEO of VivoPower, commented: “We are honoured and humbled by this recognition from the Turnaround Management Association. To be named a global awardee is a testament to the grit, creativity, winning mentality, and unwavering commitment of the hyperturnaround team. I am pleased for each of them to have been recognised as world-class in the execution of this mission. Of course, this is now history, and we are laser-focussed on the hyperscaling of VivoPower as we seek to help our customers accelerate towards net zero.”
Matt Cahir, Board Member and President of VivoPower, said: “In March 2020, at the onset of the pandemic, VivoPower’s prospects were looking very grim, with less than five weeks of cashflow and weighed down by costly legacy distractions and detractors. However, within seven months, the company’s status was secured despite COVID and without compromising creditors, because of the pace of strategic, cultural, and operational re-engineering. Now, 18 months later, VivoPower’s growth trajectory has been radically transformed into a hyperscale mission. I am delighted for the entire team.”
This year’s award-winners have made a “significant impact on the global economy during one of the most challenging times for business in generations,” according to Scott Y. Stuart, TMA Global Chief Executive Officer.
“While 2020 was a tremendously difficult year for everyone, the excellent work done by turnaround and restructuring industry professionals during this period is certainly a bright spot and something we can all be proud to celebrate,” Stuart said.
The 2021 Turnaround/Transaction of the Year Awards will be held during the TMA Annual Conference on 26-29 October in Nashville, TN. The event will also be broadcast online.
VivoPower’s Hyperturnaround in Summary
2020 was an exceptionally challenging year for VivoPower. Even before the start of the COVID-19 pandemic, the company was already going through a difficult financial and organisational restructuring, with the added distraction of costly legacy matters to contend with. To turnaround VivoPower, Arowana Founder and Executive Chairman, Kevin Chin, as majority shareholder, stepped in as CEO of the company in March 2020. He developed and implemented a hyperturnaround plan, incorporating a strategic pivot codenamed Operation Sunfish.
Kevin identified the need to “defibrillate” the company’s culture and focus the hyperturnaround mission on three key elements. First, he challenged the VivoPower team to prove the naysayers wrong, fostering a “siege mentality.” He then aligned the hyperturnaround team with performance stock unit shares which became incrementally motivational, especially as the stock price started to rise over the ensuing months.
Finally, execution, pace, and precision were improved, with defined quarterly Objectives and Key Results (OKRs) ingrained, and daily huddles to review the completion of OKRs on an execution tracker board, ensuring daily accountability. Importantly, a mindset of daily achievement was imbued: close something daily, no matter how small it is.
VivoPower also underwent a transformational strategic pivot to electric vehicles through the acquisition of Tembo e LV B.V. This allowed VivoPower to transform from a solar development and critical power services company competing in a “red ocean” to a company that delivers end-to-end sustainable energy solutions (incorporating EVs) that enable customers to decarbonise and accelerate towards net-zero status—a “blue ocean” strategy, given very few companies deliver a holistic net zero solution.
As a result, VivoPower’s share price hit an all-time high of US$24 in October 2020. This represented a 2400% increase in just seven months. It also provided the platform to consummate an oversubscribed US$28.75m capital raising which enabled the company to complete its acquisition of Tembo. It also marked the successful completion of the company’s hyperturnaround.
To read our full press release, and to keep up with all VivoPower’s releases, visit our Press Releases page.
TMA is the most diverse group of professionals in the turnaround, restructuring, and corporate health space. It is the only non-profit global organisation that allows members of the industry to build their personal brand and develop their professional network. Members include turnaround specialists, attorneys, accountants, advisors, liquidators, consultants, as well as academics, government employees, and members of the judiciary. Visit https://turnaround.org/ for more information.
There is unquestionable interest surrounding the production and sale of non-fungible tokens (NFTs). An NFT is a digital asset that represents ownership in real-world objects (these can vary from art to music, etc.). A recent sale of the artwork, “Everydays: The first 5000 days” for $69m made news headlines. NFTs are produced on the Ethereum blockchain, and among other criticisms, these are notorious for their significant energy consumption. In fact, the production of one NFT (inclusive of minting, bidding, selling, etc.) is purported to require ~369 kWh of electricity.
However, the landscape is set to change imminently. Ethereum 1.0 is in the process of transitioning to Ethereum 2.0 (Eth2) in Q1/Q2 2022, and in doing so, will move from a Proof-of-Work (POW) to a Proof-of-Stake (POS) consensus system. This transition means that the energy requirements are estimated to drop somewhere in the order of 100x to 10,000x, although we believe that the former is indeed more likely. Accordingly, NFTs are likely to reduce their environmental impact quite significantly. But how great will this reduction be? Given the uncertainty in many of our model parameters, this is a difficult metric to quantify, and one should account for uncertainty in parameter values, as well as the interplay of such parameters.
In this note, we seek to:
As of April 2021, the average price a residential customer in the US would pay for electricity is ~ 13.31 cents/kWh. However, there is significant variability in electricity costs over time, and perhaps more importantly, there is pronounced variability in the cost of electricity between states. The state with the lowest electricity rate is Louisiana, paying an average of 9.53 cents/kWh. Below, we display the spatial distribution of electricity costs across the country.
Ethereum 2.0 refers to a collection of upgrades that are designed to enhance the scalability, security, and sustainability of the Ethereum platform. The vision includes:
The table below outlines the key differences between the POS and POW consensus protocol methodologies.
When considering the cost of NFT production over the proceeding year, one notices that it is a function of several parameters, namely:
To explore the most probable cost of NFT production over the proceeding year, we design a simulation framework to study the distribution of NFT cost paths. To account for the uncertainty in aforementioned variables 1–4, we treat key parameters as random variables and run 25,000 simulations to explore the distribution of possible scenarios. Given the uncertainty in our parameters, we make the following assumptions regarding model parameters:
One can see the most probable parameter values for (1) # days until Ethereum 2.0 activation and (2) the magnitude of the reduction in electricity production after the transition below:
The simulation procedure integrates over a range of possible scenarios, taking account of their interactions, and producing paths of NFT costs. The sharp reduction in costs (with varying magnitudes) most prominently reflects the timing and impact of the switch to Ethereum 2.0.
Our simulation procedure suggests that, for one concerned with the electricity cost of producing 1 NFT per day for the proceeding year, it will likely cost ~ $2,715. One should note that this result is highly dependent on the underlying model assumptions, and should one have strong prior beliefs regarding parameter values, the simulation can easily be adjusted to account for this.
VivoPower International is pleased to announce that the company has signed a definitive agreement with GHH Group GmbH (GHH) for GHH to distribute electric light vehicles (e-LVs) in over 50 countries across Africa, Asia, Europe, and the Americas, using e-LV conversion kits from VivoPower’s wholly-owned subsidiary, Tembo e-LV B.V.
Under the agreement, GHH intends to purchase Tembo e-LV conversion kits through to December 2026. GHH will be responsible for acquiring original vehicles from Toyota, converting the vehicles to ruggedised e-LVs, using the Tembo solutions conversion kits, selling the units to end-customers, and providing ongoing servicing and maintenance.
This distribution agreement marks VivoPower’s fifth major distribution deal in 2021 for Tembo e-LV conversion kits and its largest to date by e-LV kit volumes. With the execution of this contract, VivoPower can now offer Tembo electric light vehicles to customers on six continents as it continues to advance its aim to build a global Tembo distribution network before the end of this year. The company previously executed distribution agreements with GB Auto Group in Australia, Acces Industriel Mining Inc. in Canada, and Bodiz International in Mongolia, in addition to announcing a non-binding Heads of Terms with Arctic Trucks Limited for distribution of Tembo e-LVs in Norway, Sweden, Iceland, and Finland.
Based in Germany, GHH has over 50 years of experience in the production of robust and safe vehicles for mining and tunnelling in hard and soft rock. Drawing on exclusive technology partnerships with several major industry leaders throughout the world, GHH offers the latest in cutting-edge innovation and product development to the underground mining market. Through collaboration with both clients and international partners, GHH engineers comprehensive, tailor-made solutions with their constantly expanding suite of mechanised mining equipment and a full range of well-established support services. That expertise makes GHH a natural partner to distribute Tembo e-LVs for miners seeking bespoke, comprehensive sustainable energy solutions.
The Tembo kits transform diesel-powered Toyota Land Cruiser and Hilux vehicles into ruggedised e-LVs for use in mining and other hard-to-decarbonise sectors, including construction and defence. Alongside solar generation, battery storage, and on-site power distribution, Tembo e-LV products are a key component of VivoPower’s turnkey sustainable energy solutions which help corporations achieve their decarbonisation goals.
Kevin Chin, Executive Chairman and CEO of VivoPower, said: “We are delighted to have executed this multi-country agreement with GHH, who are a trusted provider of customised technology solutions to the global mining industry. This is in keeping with our stated objective of cementing distribution agreements globally with highly credentialed partners such as GHH. With distribution partners on six continents now, Tembo e-LVs will be available globally for mining customers aiming to electrify their light vehicle operations as part of their paths to net zero mining.”
Sara Thorley, Global Marketing & Production Manager for GHH, said: “Being a global provider of heavy-duty mining machinery, we were constantly being asked about the possibility of supplying our customers with electric light vehicles. Due to the demand, we started looking at potential partners in this sector and after a substantial amount of research, we decided upon the Tembo electrification kit. First and foremost, the Tembo solution electrified the Toyota Land Cruiser and Hilux, which are the number-one light vehicles used in the mining sector globally; but secondly, the technology used created a vehicle that was smooth, efficient, safe, and very cost-effective. We are very excited about what the future holds for us and Tembo.”
To read our full press release, and to keep up with all VivoPower’s releases, visit our Press Releases page.
DDLS, Australia’s largest provider of corporate IT and process training, is pleased to announce its expansion into New Zealand with the acquisition of Auldhouse, New Zealand’s largest provider of ICT and digital skills training. The deal will add three campuses in New Zealand to the seven already established across Australia and the Philippines.
Customers of both companies will benefit from the relationship, with an unprecedented range of authorised ICT Training options from the widest selection of global vendors including the likes of Microsoft, AWS, VMware, Cisco, and Google. In addition, customers will see increased digital product offerings, a larger range of course events, a deeper pool of highly experienced and credentialed trainers, and the infrastructure across DDLS and Auldhouse to support continued growth.
Auldhouse is New Zealand’s largest and most awarded ICT and digital skills training provider and is a highly regarded brand that has been operating for over 30 years. With three campuses in Auckland, Wellington, and Christchurch, there is no other IT training provider that can match their geographic reach, breadth, depth, and regularity of public scheduled courses.
The move also marked a significant step in DDLS’s objective of growing internationally into the greater Asia-Pacific region, adding breadth to its expansion strategy with presence in Australia, the Philippines, and now New Zealand.
DDLS CEO, Jon Lang, stated that DDLS and Auldhouse have had a long-standing relationship, collaborating on numerous partnership initiatives: “Over the years we’ve built a mutual trust, respect, and confidence in the business operations and, as such, we see the business continuing to run as normal, with existing management and shareholders, Melanie Hobcraft, Leigh Richardson, and Craig Jones, all agreeing to remain in their roles at Auldhouse.”
Press release originally published here:
https://www.ddls.com.au/news/ddls-acquires-auldhouse-to-create-trans-tasman-ict-training-powerhouse/
About DDLS
DDLS is Australia’s largest provider of corporate IT and process training, with the largest portfolio of strategic partners and courses in Australia. We partner with world-class companies to help organisations and individuals in the IT industry remain up-to-date with new processes, technology, and platforms to reduce risk and enable efficient business practices. We have convenient locations in almost every capital in Australia as well as the Philippines, flexible delivery modalities, industry-accredited trainers, and state-of-the-art course material and labs to produce the highest quality learning outcomes for our clients.
About EdventureCo
EdventureCo, wholly owned by AWN Holdings Limited (ASX:AWN), is a vocational and professional education and training (VPET) platform established in Australia, with an expanding presence in Southeast Asia. It comprises market-leading businesses in blue-collar and white-collar VPET fields such as construction and IT. We have trained over 75,000 students in the past six years, and more than 196,000 in the past 26, providing them with skills and qualifications to commence their careers, upskill, or reskill. EdventureCo’s businesses are Everthought Education, DDLS, the Australian Institute of ICT, and ENS International.
Arowana’s private credit investment arm has reached agreement with ICAM Duxton STC Holdings Pty Ltd (ICAM) regarding the early settlement of its secured infrastructure debt facility for the Lucky Bay Port in South Australia. This has enabled co-investors with Arowana to realise a return in excess of the target IRR of 15%.
Lucky Bay provides an alternative grain supply channel for grain growers and other commodity suppliers on the Eyre Peninsula in South Australia. It is run by ICAM Duxton Port Infrastructure Trust (IDPIT) with ICAM as the fund manager and T-Ports as the operator, specialising in innovative logistical solutions for the export of Australian commodities. T-Ports was established by IDPIT in 2018 to initiate construction and project management of the Lucky Bay Port Facility development, with ICAM securing private debt and equity capital. The Lucky Bay Port generates road freight savings for grain growers from the Eyre Peninsula of up to $15 per tonne and 4,600 tonnes of CO2 emission savings annually.
Kevin Chin, Founder and CEO of Arowana, commented: “We are pleased to have had the opportunity to provide financial support for the development and growth of a pioneering and innovative port infrastructure project in Lucky Bay, South Australia. During the process, we were very impressed with how the ICAM and T-Ports management teams relentlessly overcame challenges posed by unexpected weather conditions and the COVID-19 pandemic in particular. As a B Corp as well as being seasoned operators who understand business challenges, we were happy to be a responsible and reasonable lending partner during challenging episodes. We would like to congratulate them on the successful close of their recent equity raising and we will be willing them every success going forward.”
ICAM’s Managing Director, Freddy Bartlett said: “Our team has shared a close working and commercial relationship with the team at Arowana in bringing this greenfields port infrastructure project to operational completion. This combined group has collaborated to work through the hurdles of a new start up infrastructure entity and the recent global challenges posed by COVID. We are extremely appreciative of the continued support from Arowana in helping to bring this South Australian port towards its ultimate growth potential.”
Johann Kenny, a Director at Arowana noted: “This transaction showcases the mutual success that can be achieved through close collaboration, open communication, and a deep understanding of the transaction’s operating and financial conditions and constraints which serve as a crucible for innovation in response to the inevitable challenges that no investment is immune to. It has been a pleasure to work with the ICAM and T Ports management teams.”
About Arowana
Arowana is a global B Corp certified group that has several operating companies and investments, including in electric vehicles, renewable energy, vocational and professional education, technology and software, venture capital, and impact asset management. Arowana’s purpose is to grow people, companies, and value.
About ICAM
ICAM is an alternative investments fund manager specialising in off-market sourcing, structuring, and active management of real estate and infrastructure assets.
ICAM has an institutional grade investment team bringing a sound investment philosophy, strong commercially focused risk-adjusted investment process, proven strategy, and an attractive track record. ICAM specialises in the investment and management of real assets in sectors such as commercial office, strategic retail, mixed use development, seniors living, and port infrastructure investments.
EBITDA Growth achieved in challenging environment
VivoPower executed strategic pivot to enter commercial Electric Vehicle market
EdventureCo delivered strong results on the back of successful digital transformation
Arowana Funds Management progressed realisation of investments
Highlights of AWN's FY2021 Annual Results and subsequent events:
Subsequent Events:
AWN's Annual Results Presentation can be viewed here.
