A low energy demand scenario for meeting the 1.5 °C target and sustainable development goals without negative emission technologies

Scenarios that limit global warming to 1.5 °C describe major transformations in energy supply and ever-rising energy demand. Here, we provide a contrasting perspective by developing a narrative of future change based on observable trends that results in low energy demand. We describe and quantify changes in activity levels and energy intensity in the global North and global South for all major energy services. We project that global final energy demand by 2050 reduces to 245 EJ, around 40% lower than today, despite rises in population, income and activity. Using an integrated assessment modelling framework, we show how changes in the quantity and type of energy services drive structural change in intermediate and upstream supply sectors (energy and land use). Down-sizing the global energy system dramatically improves the feasibility of a low-carbon supply-side transformation. Our scenario meets the 1.5 °C climate target as well as many sustainable development goals, without relying on negative emission technologies

Capacity development and knowledge transfer on the climate, land, water and energy nexus

Applying the concept of the nexus of climate, land, energy and water systems (CLEWs) to sustainable development requires the integration of knowledge from different disciplines to solve complicated multi-systems challenges. Such knowledge and expertise are not solely situated in scientific research’s theoretical realm (i.e. branch of knowledge). For the approach to be successful, integration is also required in a variety of decision spaces. The development of nexus knowledge, which we define as information related to systems’ physical, natural and socioeconomic interactions, broadly emerged from project-oriented research and case study applications, extending the system’s coverage to several resource systems, climate and governance

Modelling India’s coal production with a negatively skewed curve-fitting model

India’s coal demand is forecast to increase at a rapid pace in the future due to the country’s economic and population growth. Analyzing the scope for future production of India’s domestic coal resources, therefore, plays a vital role in the country’s development of sound energy policies. This paper presents a quantitative scenario analysis of India’s potential future coal production by using a negatively skewed curve-fitting model and a range of estimates of the country’s ultimately recoverable resources (URR) of coal. The results show that the resource base is sufficient for India’s coal production to keep increasing over the next few decades, to reach between 2400 and 3200 Mt/y at 2050, depending on the assumed value of URR. A further analysis shows that the high end of this range, which corresponds to our ‘GSI’ scenario, can be considered as the probable upper-bound to India’s domestic coal production. Comparison of production based on the ‘GSI’ scenario with India’s predicted demand shows that the domestic production of coal will be insufficient to meet the country’s rising coal demand, with the gap between demand and production increasing from its current value of about 268 Mt/y to reach 300 Mt/y in 2035, and 700 Mt/y by 2050. This increasing gap will be challenging for the energy security of India

Selected 'Starter kit' energy system modelling data for selected countries in Africa, East Asia, and South America (#CCG, 2021)

Energy system modeling can be used to develop internally-consistent quantified scenarios. These provide key insights needed to mobilise finance, understand market development, infrastructure deployment and the associated role of institutions, and generally support improved policymaking. However, access to data is often a barrier to starting energy system modeling, especially in developing countries, thereby causing delays to decision making. Therefore, this article provides data that can be used to create a simple zero-order energy system model for a range of developing countries in Africa, East Asia, and South America, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organisations, journal articles, and existing modeling studies. This means that the datasets can be easily updated based on the latest available information or more detailed and accurate local data. As an example, these data were also used to calibrate a simple energy system model for Kenya using the Open Source Energy Modeling System (OSeMOSYS) and three stylized scenarios (Fossil Future, Least Cost and Net Zero by 2050) for 2020–2050. The assumptions used and the results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work

The Climate, Land, Energy, and Water systems (CLEWs) framework: a retrospective of activities and advances to 2019

Population growth, urbanization and economic development drive the use of resources. Securing access to essential services such as energy, water, and food, while achieving sustainable development, require that policy and planning processes follow an integrated approach. The 'Climate-, Land-, Energy- and Water-systems' (CLEWs) framework assists the exploration of interactions between (and within) CLEW systems via quantitative means. The approach was first introduced by the International Atomic Energy Agency to conduct an integrated systems analysis of a biofuel chain. The framework assists the exploration of interactions between (and within) CLEW systems via quantitative means. Its multi-institutional application to the case of Mauritius in 2012 initiated the deployment of the framework. A vast number of completed and ongoing applications of CLEWs span different spatial and temporal scales, discussing two or more resource interactions under different political contexts. Also, the studies vary in purpose. This shapes the methods that support CLEWs-type analyses. In this paper, we detail the main steps of the CLEWs framework in perspective to its application over the years. We summarise and compare key applications, both published in the scientific literature, as working papers and reports by international organizations. We discuss differences in terms of geographic scope, purpose, interactions represented, analytical approach and stakeholder involvement. In addition, we review other assessments, which contributed to the advancement of the CLEWs framework. The paper delivers recommendations for the future development of the framework, as well as keys to success in this type of evaluations

Determinants of energy futures—a scenario discovery method applied to cost and carbon emission futures for South American electricity infrastructure

Energy policy and investment are commonly informed by a small number of scenarios, modelled with proprietary models and closed data-sets. It limits what levels of insight that can be derived from it. This paper overcomes these critical concerns by exploring a large number of scenarios with an open-data and open-source model to address regional mitigation policy. Focusing on South America, we translate an ensemble of long-term electricity supply scenarios into policy insights and use post-processing methods to present a systematic mapping of solution outputs to model inputs. We find demand levels, the cost of capital and the level of CO2-limits to be significant determinants of total investment cost. Low-carbon pathways are associated with low demand and low cost of capital. When cost of capital increases a shift away from wind and hydropower to natural gas and solar PV is seen. We further show that appropriate concessionary finance together with energy efficiency measures are critical—at a continental level—to unlock economic, low-carbon investment.</p

Exploring spatial and temporal resolution in energy systems modelling [Elektronisk resurs] : a model-based analysis focused on the developing electricity systems

The energy system is undergoing a transition in many parts of the world with this transition being driven by several factors such as climate change, and economic and social development. Agenda 2030, with its 17 Sustainable Development Goals (SDGs), has set the direction on where development should be focussed. There are still around 675 million people who lack access to electricity (SDG7), mainly in Sub-Saharan Africa. The energy system is also responsible for emitting most greenhouse gas (GHG) emissions and is closely connected to SDG 13, climate action.Energy models can provide insight into the implications of different interventions in the system. However, the transition also poses new challenges for energy modelling. New spatiotemporal questions arise with 1) the penetration of renewable technologies to mitigate GHG emissions, with location-specific intermittent supply options such as wind and solar PV panels, and 2) the low share of the population living near the existing electricity network in many Sub-Saharan countries and the decreasing cost of off-grid and mini-grid supply options.This change increases the number of technologies and details needed in the system which in turn increases complexity in the models. Complexity can be defined in terms of four aspects: spatial, temporal, mathematical and, system scope. However, more detail, both parametrical and structural, can introduce more potential errors and uncertainty into the model. Therefore, energy models should be as simple as possible and as complex as necessary.This thesis aims to give quantifiable and qualitative insights into the mathematical, spatial, and temporal aspects of energy systems modelling for both national and regional system scopes, along with their policy implications. The thesis explores the trade-offs between which mathematical method is applied when modelling electricity access, and the global sensitivity of parametrical and structural parameters in ESOMs.The method for achieving the aim of the thesis uses a four-step approach. First, the geospatial electrification problem is explored by developing two different models, a linear programming model, using the model generator GEOSeMOSYS, and a heuristic method, soft-linking the open-source tools OnSSET and OSeMOSYS. Second, these two models are compared in order to understand the differences between them with respect to computational effort, results, insight, and effectiveness in modelling electricity access in a developing country. Third, the linear programming model developed for this thesis is then explored using the method of Morris global sensitivity analysis to understand the importance of spatial and temporal resolution compared to other parameters such as demand, discount rate, and capital cost. Fourth and finally, the global sensitivity analysis method of factor mapping, using scenario discovery, is explored to understand parameters that determine cost and low carbon futures in the regional multi-country energy systems optimisation model ‘South America Model Base’ (SAMBA).The results show that the two methods for optimising electrification show similar trends when the demand is changed, with low demand predominantly resulting in PV panels and batteries to serve the formerly unelectrified population, while higher demand results in more grid-connected households. The demand level and profile for newly electrified households result in different service levels and possibilities for adding more appliances over time. The competitiveness of PV panels with batteries decreases significantly when the demand profile increases during the night. The two methods in this thesis have different solution times with the linear programming method having a much longer solution time, furthermore, the mathematical approaches to solve the transmission network are different, and both methods have trade-offs in their results. These trade-offs are in the mathematical approach where OnSSET uses a one-at-the-time optimisation leading to a suboptimal overall network, and GEOSeMOSYS rely on the assumption of linearity, which leads to very small incremental installations of transmission lines.The global sensitivity analysis of GEOSeMOSYS for electricity access showed that the structural parameters, spatial and temporal resolution, influence the result parameters and cannot be simplified without changing the results. The temporal resolution had a greater influence on the assessed results parameters than the spatial resolution, indicating that it is more significant. For the South American system, the parameters that determine low carbon emission pathways are low/medium demand and low/medium discount rates.This thesis has therefore shown that, even though models should be as simple as possible, the spatial and temporal resolution cannot be simplified to a one-node analysis or low temporal resolution without this affecting the results. The mathematical choice for selecting the method of electricity access was analysed and trade-offs were highlighted. The main trade-off was in the network expansion where both methods use approximations that can lead to over/underestimating the investment need. The soft-linked method is a good option to understand a higher level to explore electricity access. If the question is more complex (e.g., adding transportation, heating and cooling), then GEOSeMOSYS provides more readily available options for expanding the analysis, but at a coarse spatial resolution. Demand is a critical parameter in energy models, as is shown in this thesis, and determines both the cost and the potential for achieving low carbon futures. Therefore, including more demand functionalities (such as demand side management and price elasticity) in energy models could help to further detail future demands, and this is identified as future work.Omställningen av det globala energisystemet är pådrivet av många faktorer såsom klimatförändringar, ekonomiska och sociala faktorer. Agenda 203o, med de 17 globala målen för hållbar utveckling, har satt fokus på var den globala utvecklingen ska ligga. Denna avhandling fokuserar på globala mål 7 och 13. Det är fortfarande ca 675 miljoner människor som idag lever utan tillgång till elektricitet (globala mål 7), varav de allra flesta lever i Afrika söder om Sahara. Gällande utsläppen av växthusgaser så står energisystemet för den allra största delen, och denna utmaning att minska växthusgasutsläppen är knutet till globala mål 13.Omställningen som energisystemet står inför kräver planering för att förstå hur man ska nå dessa viktiga mål. Ett sätt att stötta planeringsprocessen är genom energimodeller som kan bidra med insikter kring olika avvägningar som kan uppstå i omställningen av systemet. Dessa energimodeller behöver dock utvecklas utifrån det nya energilandskapet, då nya energikraftslag såsom solpaneler och vindkraftverk kommer att vara viktiga framöver. Dessa energislag varierar både rumsligt och i tid, och energimodellerna behöver kunna ta hänsyn till detta för att förstå deras potential och påverkan i elsystemet. Energimodellerna behöver även fånga potentiell expansion av transmissions- och distributionsnätet, då elnätet i många länder söder om Sahara inte är fullt utbyggt. Samtidigt är energipotentialen för solpaneler, vindkraft och andra distribuerade system god och kan vara snabbare och billigare att implementera för att nå globala mål 7 än att bygga ut de nationella elnäten.Behovet av att öka energimodellernas rumsliga och temporala detaljrikedom ökar komplexiteten av dessa modeller. Generellt kan energimodellkomplexitet beskrivas genom fyra aspekter: rumslig, temporal, matematisk metod samt tillämpningsområde. Ju mer detaljrikedom som introduceras i modellerna, desto mer ökar risken för att introducera fel i modellerna, vilket i sin tur ökar osäkerheten i analysen. Energimodeller bör därför sträva efter att vara så enkla som möjligt, men samtidigt vara komplexa nog för att kunna besvara forskningsfrågan på ett adekvat sätt.Denna avhandling avser att ge insikter kring kvantitativa och kvalitativa avvägningar kring matematisk metod, rumsliga och temporala aspekter inom energimodellering, tillämpat på nationella och multinationella områden. Olika avvägningar kring valet av matematisk metod för att modellera länder med låg tillgång till elektricitet, samt känsligheten för ändringar i parametrar och struktur i energisystemmodeller utforskas i denna avhandling.Metoden för att nå målet i avhandlingen är fyrdelad: Först utvecklas två olika metoder för att modellera elektrifiering av hushåll som saknar elektricitet i ett land där tillgången till modern energi är låg. Den första kopplar samman två modeller med öppen källkod: OnSSET och OSeMOSYS. Den andra metoden utvecklar sättet på vilken den rumsliga dimensionen kan bättre modelleras i OSeMOSYS och kallas för GEOSeMOSYS. En jämförelse av dessa två metoder görs för att förstå skillnader i resultat, beräkningstid, vilka insikter som modellerna kan ge och hur effektiva modellerna är att modellera elektrifiering av länder med låg tillgång till modern energi. För att förstå känsligheten i elektrifieringsmodellen som utvecklats i denna avhandling, GEOSeMOSYS, utvärderas den rumsliga och temporala känsligheten mot andra parametrar såsom kostnader, diskonteringsränta och olika efterfrågenivåer. Slutligen används känslighetsanalys för att förstå vilka parametrar som kan möjliggöra låga kostnader samt låga koldioxidutsläpp i den sydamerikanska kontinenten ur ett långtidsperspektiv.Resultaten för elektrifiering av länder med låg access till elektricitet från jämförelsen av de två metoderna utvecklade i denna avhandling visar liknande trender, med hög penetration av solpaneler när elbehovet är lågt, men att när behovet ökar så blir det mer kostnadsekonomiskt att bygga ut elnätet. GEOSeMOSYS tar ca 35 gånger längre tid att optimera jämfört med den sammankopplade metoden med OSeMOSYS och OnSSET. Metoden för att optimera elnätverket för de två olika metoderna skiljer sig åt och ger därför något olika utfall, båda med kompromisser i exakthet. Detta beror på den matematiska metoden där OnSSET optimerar en-åt-gången, vilket kan ge suboptimala resultat med exempelvis parallella transmissionsledningar. GEOSeMOSYS å andra sidan bygger på antagandet om att utbyggnaden av elsystemet är linjärt, vilket leder till väldigt små installationer av kapacitet för transmissionsnätet till relativt låga kostnader.Känslighetsanalysen visar att både de rumsliga och temporala dimensionerna i energimodellerna påverkar utfallet, vilket gör att de inte kan modelleras på en väldigt låg rumslig eller temporal nivå utan att påverka resultaten. En högre känslighet påvisades för den temporala nivån jämfört med den rumsliga nivån. Vidare visar känslighetsanalysen för Sydamerika att scenarion med låga koldioxidutsläpp kan nås genom att hålla elbehovet och diskonteringsräntan på en låg nivå.Sammanfattningsvis så har denna avhandling visat att även om modeller ska vara så enkla som möjligt så behöver avvägningar göras och komplexitet bibehållas, speciellt gällande den temporala dimensionen. Båda modellerna för att elektrifiera länder med låg tillgång till el som presenterats i avhandlingen har olika kompromisser. Den sammankopplade modellen med OnSSET och OSeMOSYS är bra för att få förståelse för den optimala teknologimixen på en övergripande nivå. Om frågan är mer komplex och innehåller fler energiflöden (än bara elektricitet) så ger GEOSeMOSYS mer flexibilitet att modellera det tillsammans med access till el, men på bekostnad av en lägre rumslig nivå. Elbehovet som modelleras har visats i denna avhandling ha en väldigt stor effekt på alla resultat och därför bör framtida arbete fokusera på att vidareutveckla detaljer kring behovssidan (såsom lastomflyttning och priselasticitet).</p