From Kyoto to Paris - a review of South Africa's climate change response with particular emphasis on the Clean Development Mechanism and envisaged transition to the Sustainable Development Mechanism

The climate change timeline is at critical juncture as policymakers, academics and other climate-related stakeholders are contemplating the transition from the Kyoto era to the advent of the landmark 2015 Paris Agreement on Climate Change (the Paris Agreement). The study focuses in particular on tracing the development of and learning from past experience with the Clean Development Mechanism (CDM) - one of the flexible mechanisms provided for in the Kyoto Protocol and tailored specifically for reducing emissions in developing countries. This is undertaken with a view to drawing lessons for its apparent successor, the Sustainable Development Mechanism (SDM) provided for in the Paris Agreement. The study is undertaken against the backdrop of the theoretical framework of market-based instruments supplementing the traditional command-and-control approach to reducing carbon emissions, specifically in the area of environmental taxes and carbon offsets. While it is acknowledged that the major difference between the proposed SDM and the existing CDM is that carbon markets will no longer be limited to developed country parties in that developing countries will also be able to participate, many uncertainties remain. Moreover, while it is uncertain at the time of writing (mid-2017) whether the CDM will continue to exist alongside the SDM or will be replaced by it, the study investigates, among other things, questions around if and how the SDM refines and streamlines the CDM. It considers these questions in the context of not only the relevant international instruments, but more specifically against the backdrop of South Africa's climate-related laws and policy frameworks. It is posited that the CDM - and by extension the SDM - will come increasingly under the spotlight in South Africa, as it will serve as a useful mechanism for reducing (or offsetting) the impending carbon tax liability. The thesis finds that many of the principles listed for the SDM mirror those of the CDM. Yet, some sort of transition from Kyoto to Paris will be required to ensure that the SDM will realise its potential to mitigate emissions and support sustainable development

Graph pattern matching on social network analysis

Graph pattern matching is fundamental to social network analysis. Its effectiveness for identifying social communities and social positions, making recommendations and so on has been repeatedly demonstrated. However, the social network analysis raises new challenges to graph pattern matching. As real-life social graphs are typically large, it is often prohibitively expensive to conduct graph pattern matching over such large graphs, e.g., NP-complete for subgraph isomorphism, cubic time for bounded simulation, and quadratic time for simulation. These hinder the applicability of graph pattern matching on social network analysis. In response to these challenges, the thesis presents a series of effective techniques for querying large, dynamic, and distributively stored social networks. First of all, we propose a notion of query preserving graph compression, to compress large social graphs relative to a class Q of queries. We then develop both batch and incremental compression strategies for two commonly used pattern queries. Via both theoretical analysis and experimental studies, we show that (1) using compressed graphs Gr benefits graph pattern matching dramatically; and (2) the computation of Gr as well as its maintenance can be processed efficiently. Secondly, we investigate the distributed graph pattern matching problem, and explore parallel computation for graph pattern matching. We show that our techniques possess following performance guarantees: (1) each site is visited only once; (2) the total network traffic is independent of the size of G; and (3) the response time is decided by the size of largest fragment of G rather than the size of entire G. Furthermore, we show how these distributed algorithms can be implemented in the MapReduce framework. Thirdly, we study the problem of answering graph pattern matching using views since view based techniques have proven an effective technique for speeding up query evaluation. We propose a notion of pattern containment to characterise graph pattern matching using views, and introduce efficient algorithms to answer graph pattern matching using views. Moreover, we identify three problems related to graph pattern containment, and provide efficient algorithms for containment checking (approximation when the problem is intractable). Fourthly, we revise graph pattern matching by supporting a designated output node, which we treat as “query focus”. We then introduce algorithms for computing the top-k relevant matches w.r.t. the output node for both acyclic and cyclic pattern graphs, respectively, with early termination property. Furthermore, we investigate the diversified top-k matching problem, and develop an approximation algorithm with performance guarantee and a heuristic algorithm with early termination property. Finally, we introduce an expert search system, called ExpFinder, for large and dynamic social networks. ExpFinder identifies top-k experts in social networks by graph pattern matching, and copes with the sheer size of real-life social networks by integrating incremental graph pattern matching, query preserving compression and top-k matching computation. In particular, we also introduce bounded (resp. unbounded) incremental algorithms to maintain the weighted landmark vectors which are used for incremental maintenance for cached results

Applications of Sampling and Estimation on Networks

Networks or graphs are fundamental abstractions that allow us to study many important real systems, such as the Web, social networks and scientific collaboration. It is impossible to completely understand these systems and answer fundamental questions related to them without considering the way their components are connected, i.e., their topology. However, topology is not the only relevant aspect of networks. Nodes often have information associated with them, which can be regarded as node attributes or labels. An important problem is then how to characterize a network w.r.t. topology and node label distributions. Another important problem is how to design efficient algorithms to accomplish tasks on networks. Since nodes often have attributes, an interesting avenue for investigation consists in learning and exploiting existing correlations between node and neighbor attributes for accomplishing a task more efficiently. One of the challenges faced when studying networks in the wild is the fact that in general their topology and information associated with its nodes cannot be directly obtained. Thus, one must resort to collecting the data, but when obtaining the entire network is infeasible, sampling and estimation are the best option. This dissertation investigates the use of sampling and estimation to characterize networks and to accomplish a particular task. More precisely, we study (i) the problem of characterizing directed and undirected networks through random walk-based sampling, (ii) the problem of estimating the set-size distribution from an information-theoretic standpoint, which has application to characterizing the in-degree distribution in large graphs, and (iii) the problem of searching networks to find nodes that exhibit a specific trait while subject to a sampling budget by learning a model from node attributes and structural properties, which has application to recruiting in social networks

Energy Bridge Builder: Finland – Namibia Cooperation in Energy Business

This study is a result of a project initiated by the author in response to a looming electric power crisis in Namibia. Namibia has traditionally imported over half (at times up to 60%) of the electricity consumed in the country from neighbouring countries through Southern African Power Pool agreements. However, the exporting countries are facing power shortages as local demand continues to grow. It is against this backdrop that Namibia has embarked on a mission to become self-sufficient in power production. The objective of the study is to promote business collaboration between energy companies and public sector in Finland and energy companies and public sector in Namibia. The objective is achieved by assessing the availability of renewable energy resources in Namibia, identifying major actors, investor networks, energy needs, and barriers to entry into the Namibian energy industry. The primary aim of the research is to build a networking platform for energy stakeholders from the two countries and ultimately create business relationships. As a result, the study proposes entry modes into Namibia for Finnish companies. The secondary aim is to promote education and research cooperation between the University of Vaasa and the Namibia University of Science and Technology. The empirical data was collected through a survey distributed to Namibian energy stakeholders. The study revealed that the country is rich in renewable energy resources and has an established government-supported energy market. The study concluded that there are numerous business opportunities for Finnish energy companies in Namibia, and the best entry strategy into this young market is through strategic partnerships with local companies. Moreover, Namibian companies and the public sector are willing and able to contribute financially and strategically to partnerships with Finnish companies.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Jointly integrating current context and social influence for improving recommendation

La diversité des contenus recommandation et la variation des contextes des utilisateurs rendent la prédiction en temps réel des préférences des utilisateurs de plus en plus difficile mettre en place. Toutefois, la plupart des approches existantes n'utilisent que le temps et l'emplacement actuels séparément et ignorent d'autres informations contextuelles sur lesquelles dépendent incontestablement les préférences des utilisateurs (par exemple, la météo, l'occasion). En outre, ils ne parviennent pas considérer conjointement ces informations contextuelles avec les interactions sociales entre les utilisateurs. D'autre part, la résolution de problèmes classiques de recommandation (par exemple, aucun programme de télévision vu par un nouvel utilisateur connu sous le nom du problème de démarrage froid et pas assez d'items co-évalués par d'autres utilisateurs ayant des préférences similaires, connu sous le nom du problème de manque de donnes) est d'importance significative puisque sont attaqués par plusieurs travaux. Dans notre travail de thèse, nous proposons un modèle probabiliste qui permet exploiter conjointement les informations contextuelles actuelles et l'influence sociale afin d'améliorer la recommandation des items. En particulier, le modèle probabiliste vise prédire la pertinence de contenu pour un utilisateur en fonction de son contexte actuel et de son influence sociale. Nous avons considérer plusieurs éléments du contexte actuel des utilisateurs tels que l'occasion, le jour de la semaine, la localisation et la météo. Nous avons utilisé la technique de lissage Laplace afin d'éviter les fortes probabilités. D'autre part, nous supposons que l'information provenant des relations sociales a une influence potentielle sur les préférences des utilisateurs. Ainsi, nous supposons que l'influence sociale dépend non seulement des évaluations des amis mais aussi de la similarité sociale entre les utilisateurs. Les similarités sociales utilisateur-ami peuvent être établies en fonction des interactions sociales entre les utilisateurs et leurs amis (par exemple les recommandations, les tags, les commentaires). Nous proposons alors de prendre en compte l'influence sociale en fonction de la mesure de similarité utilisateur-ami afin d'estimer les préférences des utilisateurs. Nous avons mené une série d'expérimentations en utilisant un ensemble de donnes réelles issues de la plateforme de TV sociale Pinhole. Cet ensemble de donnes inclut les historiques d'accès des utilisateurs-vidéos et les réseaux sociaux des téléspectateurs. En outre, nous collectons des informations contextuelles pour chaque historique d'accès utilisateur-vidéo saisi par le système de formulaire plat. Le système de la plateforme capture et enregistre les dernières informations contextuelles auxquelles le spectateur est confronté en regardant une telle vidéo.Dans notre évaluation, nous adoptons le filtrage collaboratif axé sur le temps, le profil dépendant du temps et la factorisation de la matrice axe sur le réseau social comme tant des modèles de référence. L'évaluation a port sur deux tâches de recommandation. La première consiste sélectionner une liste trie de vidéos. La seconde est la tâche de prédiction de la cote vidéo. Nous avons évalué l'impact de chaque élément du contexte de visualisation dans la performance de prédiction. Nous testons ainsi la capacité de notre modèle résoudre le problème de manque de données et le problème de recommandation de démarrage froid du téléspectateur. Les résultats expérimentaux démontrent que notre modèle surpasse les approches de l'état de l'art fondes sur le facteur temps et sur les réseaux sociaux. Dans les tests des problèmes de manque de donnes et de démarrage froid, notre modèle renvoie des prédictions cohérentes différentes valeurs de manque de données.Due to the diversity of alternative contents to choose and the change of users' preferences, real-time prediction of users' preferences in certain users' circumstances becomes increasingly hard for recommender systems. However, most existing context-aware approaches use only current time and location separately, and ignore other contextual information on which users' preferences may undoubtedly depend (e.g. weather, occasion). Furthermore, they fail to jointly consider these contextual information with social interactions between users. On the other hand, solving classic recommender problems (e.g. no seen items by a new user known as cold start problem, and no enough co-rated items with other users with similar preference as sparsity problem) is of significance importance since it is drawn by several works. In our thesis work, we propose a context-based approach that leverages jointly current contextual information and social influence in order to improve items recommendation. In particular, we propose a probabilistic model that aims to predict the relevance of items in respect with the user's current context. We considered several current context elements such as time, location, occasion, week day, location and weather. In order to avoid strong probabilities which leads to sparsity problem, we used Laplace smoothing technique. On the other hand, we argue that information from social relationships has potential influence on users' preferences. Thus, we assume that social influence depends not only on friends' ratings but also on social similarity between users. We proposed a social-based model that estimates the relevance of an item in respect with the social influence around the user on the relevance of this item. The user-friend social similarity information may be established based on social interactions between users and their friends (e.g. recommendations, tags, comments). Therefore, we argue that social similarity could be integrated using a similarity measure. Social influence is then jointly integrated based on user-friend similarity measure in order to estimate users' preferences. We conducted a comprehensive effectiveness evaluation on real dataset crawled from Pinhole social TV platform. This dataset includes viewer-video accessing history and viewers' friendship networks. In addition, we collected contextual information for each viewer-video accessing history captured by the plat form system. The platform system captures and records the last contextual information to which the viewer is faced while watching such a video. In our evaluation, we adopt Time-aware Collaborative Filtering, Time-Dependent Profile and Social Network-aware Matrix Factorization as baseline models. The evaluation focused on two recommendation tasks. The first one is the video list recommendation task and the second one is video rating prediction task. We evaluated the impact of each viewing context element in prediction performance. We tested the ability of our model to solve data sparsity and viewer cold start recommendation problems. The experimental results highlighted the effectiveness of our model compared to the considered baselines. Experimental results demonstrate that our approach outperforms time-aware and social network-based approaches. In the sparsity and cold start tests, our approach returns consistently accurate predictions at different values of data sparsity

Comparative study of renewable energy policies between Ecuador and Germany. Shifting from FITs to Auctions

Policy measures are essential tools for the transition from fossil fuels to renewable energy sources in the electricity generation. Feed-in tariffs (FITs) are the most used policy for support of renewable technologies globally. Nevertheless, Ecuador phased-out FITs in 2016 and adopted auctions in 2015. Ecuador’s implementation of auctions reflects an approach with little relation to practices in other countries and without a technology-specific design. On the other hand, Germany demonstrates a long trajectory in policy-making with vast experience in FITs. Moreover, in 2017 Germany adopted auctions as the official policy to support renewable projects with a scope larger than 750 kW. However, FITs are still in use and complement auctions. This thesis analyses and compares the country-specific contexts where these policies are implemented through the lens of a multi-level perspective framework. As a result, the most important success factors in Germany have been identified: long-term planning, institutional continuity, legislative stability and principally an actively participating society, which is environmentally aware. Obstacles for the transition in Ecuador are institutional discontinuity, legislative inconsistency, the lack of long-term planning and absence of society as actors of the transition. Due to Germany’s pioneering role plus the successful growth of renewable sources during the last twenty years, their policy implementations appear desirable to adopt in other contexts. Therefore, a prospective transferability of the auction policy from Germany to Ecuador is additionally analyzed

Investigation of small scale power generation and briquette production from biomass, involving cases from Bolivia

Fossil fuels are still the major source of power in the world. The unabated increase in greenhouse gas (GHG) emissions over the years has accelerated climate change. Various international agreements and treaties like the Paris Agreement in 2015 have aimed at reduction of emissions. But to effectively combat climate change, collective efforts at every scale are required. On the other hand, there are communities who are financially insecure and suffer from energy poverty. Incidentally, many of these communities are also the people who are most prone to the effects of climate change. Providing renewable power to these communities will make communities more resilient and also create opportunities for financial and social development. The focus of the work in this thesis has been the investigation of production of decentralised power and briquettes in the context of settler communities living on the edge of the Bolivian Amazon forest. The needs of such communities are oftentimes ignored by institutions, and the challenging geographic conditions further complicate matters. Agroforestry plantations have been established as a means for reforestation and poverty alleviation. The residues available from local forestry plantations are considered as the feedstock. Different biomass energy conversion pathways were examined and compared. A CHP plant of 50 kW electric output and a briquetting facility of 200 kg/hour capacity have been proposed. Gasification and combustion with ORC turbine were found to be technologically feasible and commercially available for a plant of this scale. The technical parameters, performance characteristics and economics of a plant based on both selected conversion methods were analysed. The overall electric efficiency for a gasifier power plant was higher at 21 % compared to 10% for an ORC power plant. However, the recovered heat is higher in an ORC plant, leading to a total efficiency of 73%. The gasifier plant was slightly deficient in terms of providing the thermal energy needed for drying, hence the output of briquettes was lower. The daily biomass consumption (at 40% MC) for briquetting, and power production for gasifier and ORC based plants, were about 3.5 tons and 4.2 tons respectively. According to estimates, the biomass supply requirement is easily met from the plantations, and leaves potential for scaling up of operations. The total investment cost, annual recurring costs and revenues earned for both systems were computed. The indicators of NPV, IRR and discounted payback period were utilised to compare the economic feasibility. The initial costs associated with a gasifier powered plant was lower. At a small scale, the gasifier option seems to be an economically suitable option. However, on an operational and performance basis, ORC systems are more reliable. The economic feasibility was found to be heavily reliant on the selling price of briquettes. By ensuring an appropriate selling price for briquettes, both the ORC and gasifier configurations of the plant are economically feasible. A plant like this is expected to provide employment and alternative economic benefits to the local communities which can lead to positive change in society and development of services. Moreover, the project will lead to savings of over 1000 tons of CO2 equivalent GHG emissions over a 20-year lifespan. There exist certain barriers in the path of such projects, especially due to low prices of electricity and fossil fuels. The government is in the process of developing policies and regulations to support renewable energy production which can incentivise such projects and decrease the payback period

Effekter av tilgang til fornybare energikilder og teknologi på rurale husholdningers energiforbruk og på miljøet i Etiopia

Access to modern, affordable, and reliable energy and clean cooking facilities is critical for Ethiopia to drive its economic development, reduce poverty and curb the negative environmental and health impacts of traditional and unsustainable use of solid biomass fuels. To that end, the government of Ethiopia has devoted considerable efforts in recent years to improving rural access to electricity, and the dissemination of household biogas systems, solar photovoltaic (PV) systems and improved biomass cookstoves (ICSs). In light of these efforts, the present thesis aims to investigate and empirically examine the effects of access to modern and renewable energy sources and technologies on the rural households’ energy use patterns, well-being, and the environment in southern Ethiopia. In doing so, the thesis seeks to shed new light on the nexus between renewable energy access and household energy transition in rural sub-Saharan Africa in the face of climate change. The research was carried out mainly in four rural districts of Southern Ethiopia and data were collected from a comprehensive cross-sectional study (survey) of sample households, direct field assessments, and energy consumption measurements. The first paper systematically reviews and analyses existing empirical evidence on the potential environmental impacts of small-scale renewable energy technologies (SRETs): biogas, ICSs, and solar PVs in East Africa by taking Ethiopia, Kenya, Tanzania and Uganda as case studies. The results showed that SRETs have considerable potential for reducing household consumption of traditional fuels; thereby lessening forest degradation and the subsequent carbon dioxide (CO2) emission at local level. Our conservative estimates, based on the evidence, indicated that the biogas plants and ICSs disseminated in each country until 2015, had a combined potential of saving 0.31 to 3.10 million tons (Mt) of woodfuel and reducing emissions of 0.56 to 5.67 Mt of CO2 equivalent (CO2e) per country per year. However, when compared with the annual biomass energy consumptions and CO2 emissions of each country, the biogas and ICSs disseminated till 2015 did not appear to offset more than 7.2% of the total woody biomass energy consumed and 3.8% of the total CO2e emitted by the respective countries per year. In light of the evidence from the systematic review in paper I, in paper II we analysed the current utilization rate, performance, and impact of domestic biogas systems in rural southern Ethiopia based on direct field studies and surveys in four districts. The results showed that despite growing efforts, the uptake and utilization of biogas technology is yet very low. Out of the total 32 digesters directly investigated, only 21 (65.63%) were found functional. The average quantity of biogas produced from a 6m3 functional plant was estimated to be 0.61 m3/day. This suggests that the current level of biogas use could substitute the consumption of 632 kg of fuelwood and 25 L of kerosene per household per year. However, comparative analysis of the total energy consumption of biogas user and non-user households revealed that the effect of biogas use on household fuelwood and kerosene consumptions, and energy transition was insignificant. Paper III extended the in-depth investigation and examined the potential fuel savings, economic and environmental co-benefits of three ICSs (Mirt, Gonziye, and Tikikil from a survey of 605 sample households and direct kitchen cooking observations to 133 ICSs users. The study finds that compared with the traditional open-fire tripod, the three ICSs studied could reduce household fuelwood consumption on average by 1.72 to 2.08 tons (t)/stove/year. The fuelwood savings translate to an estimated CO2e emission reduction of 2.82 to 3.43 tCO2e per stove per year. The results from the cost-benefit analysis (CBA) showed that usage of these ICSs could provide a net economic return of between US$317 and$460 during the 2 to 5 years lifespan of the stoves. The study highlighted that beyond improving the energy efficiency and well-being of rural households, ICSs are an essential component of the national and global strategies for GHGs emissions abatement. In paper IV we explored the impacts of rural electrification with solar PV systems in the study districts based on the survey data and direct field assessment of 137 solar PVs and lanterns. The findings indicated that solar-electrified households consume on average 43.68 litres less kerosene, and emit 107 kg less CO2 and 2.72 kg less Black Carbon (BC) per year compared with non-electrified households (neither grid nor solar light). This reduction in kerosene consumption and the access to electricity from the solar PVs could enable a solar user household to save between US$65 and$75 per year from the avoided energy expenditures and mobile charging costs. The new access to electricity and solarlighting has also reduced the health risks of rural families from kerosene wick lamps and allowed small-businesses to generate more income. The study concluded that solar PVs and lanterns are improving rural households’ wellbeing and access to clean lighting, and therefore should be further integrated into the national energy systems. However, the sustainability and effectiveness of solar PVs faces serious challenges from poor-quality and counterfeit products in the market, high cost of quality-verified products, lack of after-sales maintenance services, and limited access to credit financing services. In paper V, we analysed the current patterns of rural households’ energy consumption and the share of modern and clean fuels to examine the overall effect of access to modern and renewable sources and technologies on rural household energy use and transition. The results showed that more than 97% of the households still rely on traditional solid biomass fuels, particularly fuelwood (90.7%) as the primary fuel for cooking and baking Injera (Ethiopian bread). In contrast, the use of biogas and electricity for cooking was limited. On the other side, 50% use kerosene, 29% grid electricity, 19% solar, and 1.98% biogas as primary energy sources for lighting. Of the total 87, 172 MJ energy estimated to be consumed by a rural household per year, energy derived from traditional biomass fuels accounted for 85, 278 MJ (97.83%); while energy from modern and clean sources (electricity, biogas and solar) combined accounted for only 830 MJ (≈ 1%). The findings indicated that the recent efforts of Ethiopia to improving the rural access to modern and renewable energy sources have led to significant lighting energy substitution and partial transition from kerosene oil-based towards clean lighting fuels. However, we found no evidence of substantive energy substitution to suggest that the heavy dependence on traditional solid biomass fuels for cooking and baking end-uses is declining. Given the findings in paper V, in paper VI, we examined the major determinants of rural household’s energy choices for cooking and lighting by using Pearson’s Chi-square (χ2) test and Multivariate probit model. The results indicated that rural household’s primary cooking fuels are statistically significantly associated with the household size, distance to wood source, location, and income level. Empirical results of the multivariate analysis showed that rural households’ energy choices for lighting are significantly influenced by income level, family size, location, educational status, distance to market, road access. We find that wealthier and more educated households residing near road access were more likely to use clean lighting energy such as electricity and solar power; while poorer households residing in areas with limited road access use kerosene and dry-cell battery. However, the results also indicated that high-income level and grid-connection have not led households to completely forgo the use of traditional cooking and lighting fuels. This pattern appears to observe the energy-stacking model as opposed to the energy-ladder model of complete fuel-switching. While income remains a principal factor, the study finds that several non-income factors also play a major role in determining the energy choices and energy transition of rural households in developing countries. Overall, this PhD thesis provides new empirical evidence and fresh insights to inform decision making and energy planning on the socio-economic, environmental, and energy transition effects of access to renewable energy sources and improved cookstoves; and the associated drivers, challenges, and determinants in the context of rural sub-Saharan Africa. The thesis has shown that increased access and use of modern and renewable energy sources such as electricity and solar in rural areas of developing countries can lead to significant energy substitution and transition from kerosene towards clean and quality lighting. It has also revealed that promoting the use of ICSs is a viable option and an essential component of the strategy for reducing deforestation, mitigation of climate change, and sustainable use of biomass in sub-Saharan Africa. The low rate of utilization and impact from household biogas systems, on the other hand, signifies that thorough re-examining of existing dissemination approaches and operational practices is critical. Most importantly, the thesis has highlighted that the nexus between access to modern and renewable energy; and household energy transition in rural sub-Saharan Africa is complex and non-linear. As such, traditional biomass fuels will likely remain the primary energy sources of even the wealthiest households that are connected to the grid. The implication is that solid biomass-energy dependent countries like Ethiopia need to critically address the growing demand for biomass fuels through developing sustainable and diversified bio-energy sources, energy-saving and affordable cooking technologies, and decentralized renewable rural hybrid energy systems alongside the current efforts of improving rural access to grid electricity. Although the data for this study is primarily from rural southern Ethiopia, the conclusions and policy implications drawn can have a wider application in the broader context of rural sub-Saharan Africa.NORAD ; National MRV Capacity Building Project of Hawassa University, Wondo Genet College of Forestry and Natural Resources (WGCF-NR

Sustainable Energy United in Diversity: Challenges and Approaches in Energy Transition in the EU

This book focuses on the relationship between environmental protection and the production, distribution and consumption of energy and the regulatory and policy challenges that are felt in the transition to a sustainable energy system and more generally a low carbon economy.Today, an increasingly important part of EU environmental law concerns thisenergy transition, triggering regulatory experimentation and judicial activity atmyriad levels. ‘Sustainable energy united in diversity – Challenges andapproaches in energy transition in the European Union’ contains contributionsby Sandy Gaines, Wybe Douma, Yelena M. Gordeeva, Vicky Karageorgou, RalphFrins, Hendrik Schoukens, Ellen Margrethe Basse, Birgitte Egelund Olsen, HelleTegner Anker, Magali Dreyfus, Louise du Toit, Wolfgang Köck, Hartmut Kahland Nicolas Pradel showing the widely differing national policy initiatives in thediverse legal frameworks working – more or less successfully – to attaining theoverarching 20-20-20-objectives the EU has set itself. With a foreword byRebecca Harms, co-president of the Greens/EFA Group and member of theEuropean Parliament, this book not only explores the national and Europeanregulatory activities, but also finds a regulatory dynamic that identifies aEuropean framework as a valuable way forward