Global high-resolution growth projections dataset for rooftop area consistent with the shared socioeconomic pathways, 2020-2050.

Assessment of current and future growth in the global rooftop area is important for understanding and planning for a robust and sustainable decentralised energy system. These estimates are also important for urban planning studies and designing sustainable cities thereby forwarding the ethos of the Sustainable Development Goals 7 (clean energy), 11 (sustainable cities), 13 (climate action) and 15 (life on land). Here, we develop a machine learning framework that trains on big data containing ~700 million open-source building footprints, global land cover, road, and population datasets to generate globally harmonised estimates of growth in rooftop area for five different future growth narratives covered by Shared Socioeconomic Pathways. The dataset provides estimates for ~3.5 million fishnet tiles of 1/8 degree spatial resolution with data on gross rooftop area for five growth narratives covering years 2020-2050 in decadal time steps. This single harmonised global dataset can be used for climate change, energy transition, biodiversity, urban planning, and disaster risk management studies covering continental to conurbation geospatial levels.

A deep learning architecture for energy service demand estimation in transport sector for Shared Socioeconomic Pathways.

Meeting current global passenger and freight transport energy service demands accounts for 20% of annual anthropogenic CO2 emissions, and mitigating these emissions remains a considerable challenge for climate policy. Pursuant to this, energy service demands play a critical role in the energy systems and integrated assessment models but fail to get the attention they warrant. This study introduces a novel custom deep learning neural network architecture (called TrebuNet) that mimics the physical process of firing a trebuchet to model the nuanced dynamics inherent in energy service demand estimation. Here we show, how TrebuNet is designed, trained, and used to estimate transport energy service demand. We find that the TrebuNet architecture shows superior performance compared with traditional multivariate linear regression and state of the art methods like densely connected neural network, Recurrent Neural Network, and Gradient Boosted machine learning algorithms when evaluated for regional demand projection for all modes of transport demands at short, decadal, and medium-term time horizons. Finally, TrebuNet introduces a framework to project energy service demand for regions having multiple countries spanning different socio-economic development pathways which can be replicated for wider regression-based task for timeseries having non-uniform variance.

Systematically reviewing the use of participatory methods in energy system modelling and planning literature.

This article outlines the systematic review process undertaken to identify what progress has been made on the integration of participatory methods into energy system modelling and planning. As an emergent field that combines technical / social sciences, it presented a couple of interesting challenges. Firstly, the issue of language emerged as there is a wide range of different terms that may be used to refer to both the involvement of stakeholders in research and energy system modelling and planning tools. This required careful consideration of the research questions and search criteria during the initial scoping exercise. On from this, a conceptual framing of what a meaningful stakeholder participation involves was developed to help define the criteria for inclusion in this study and assess the literature to date. Finally, in synthesizing the literature reviewed to provide an overview of the field, several creative data visualizations were produced.•Systematic review process customized to identify literature covering the integration of participatory methods and energy system modelling and planning tools.•Conceptual framework developed to define criteria for inclusion in the compiled database.

Passenger transport demand, fuel consumption, and emissions data for the Irish Passenger Transport Emissions and Mobility (IPTEM) model.

These data and analyses support the research article "How and Why We Travel - Mobility Demand and Emissions from Passenger Transport (O'Riordan et al., 2022). This article refers to a spreadsheet model, the Irish Passenger Transport Emissions and Mobility Model (IPTEM V2.9). The spreadsheet model is available for download from Zenodo (O'Riordan et al., 2022). The model and the underlying data, details the passenger transport demand by trip purpose (work, shopping, education etc.,), mode type (car, rail, bus, cycling, walking) and trip distance for Ireland over the period of 2009-2019. Passenger occupancy rates for public transport modes in Ireland, CO2 emissions intensities and annual CO2 emissions are also included in the Data in Brief. Assumptions and equations used to develop the IPTEM V2.9 are available in the Experimental design, materials, and methods section.

Mapping emergent public engagement in societal transitions: a scoping review.

BACKGROUND: Transition discourses are gaining prominence in efforts to imagine a future that adequately addresses the urgent need to establish low carbon and climate resilient pathways. Within these discourses the 'public' is seen as central to the creation and implementation of appropriate interventions. The role of public engagement in societal transformation while essential, is also complex and often poorly understood. The purpose of this paper is to enhance our understanding regarding public engagement and to address the often superficial and shallow policy discourse on this topic. MAIN TEXT: The paper offers a review of evolving literature to map emergent public engagement in processes of transition and change. We adopt a pragmatic approach towards literature retrieval and analysis which enables a cross-disciplinary and cross-sectoral review. We use a scoping review process and the three spheres of transformation framework (designated as the practical, political and personal spheres) to explore trends within this complex research field. The review draws from literature from the last two decades in the Irish context and looks at emergence and evolving spaces of public engagement within various systems of change including energy, food, coastal management and flood adaptation, among others. CONCLUSIONS: The results highlight the siloed and fragmented way in which public engagement in transitions is carried and we propose a more cross-sectoral and cross-disciplinary approach which depends on bringing into dialogue often contrasting theories and perspectives. The paper also illustrates some shifting engagement approaches. For instance, nexus articles between the practical and political spheres suggest deeper forms of public engagement beyond aggregated consumer behaviour to align technological delivery with institutional and societal contexts. While most articles in the practical sphere draw largely on techno-economic insights this influence and cross-disciplinarity is likely to draw in further innovations. Nexus articles between the political and personal sphere are also drawing on shifting ideas of public engagement and largely stress the need to disrupt reductive notions of engagement and agency within our institutions. Many of these articles call attention to problems with top-down public engagement structures and in various ways show how they often undermine and marginalise different groups.

High resolution global spatiotemporal assessment of rooftop solar photovoltaics potential for renewable electricity generation.

Rooftop solar photovoltaics currently account for 40% of the global solar photovoltaics installed capacity and one-fourth of the total renewable capacity additions in 2018. Yet, only limited information is available on its global potential and associated costs at a high spatiotemporal resolution. Here, we present a high-resolution global assessment of rooftop solar photovoltaics potential using big data, machine learning and geospatial analysis. We analyse 130 million km2 of global land surface area to demarcate 0.2 million km2 of rooftop area, which together represent 27 PWh yr-1 of electricity generation potential for costs between 40-280 $ MWh-1. Out of this, 10 PWh yr-1 can be realised below 100 $ MWh-1. The global potential is predominantly spread between Asia (47%), North America (20%) and Europe (13%). The cost of attaining the potential is lowest in India (66 $ MWh-1) and China (68 $ MWh-1), with USA (238 $ MWh-1) and UK (251 $ MWh-1) representing some of the costliest countries.

A compiled dataset of the energy usage indicators and unit energy consumption values available in Ireland.

This dataset supports the analysis outlined in (McGookin et al., 2021) "An innovative approach for estimating energy demand and supply to inform local energy transitions" [1]. It consists of four key elements: a range of different energy usage indicators (e.g. the number of employees or cars in a region), national unit energy consumption values, energy supply fuel shares per sector, and an overview of the housing stock. Firstly, the range of socio-economic statistics used as indicators of energy demand are primarily gathered from the Central Statistics Office's (CSO), along with sector specific sources like the Department of Transport or Fisheries. Secondly, the national energy demand and supply in the five sectors of agriculture and fishing, industry, residential, services and transport comes from the national reporting body Sustainable Energy Authority of Ireland. These two datasets are then used to form the national unit consumption figures for a range of indicators in each sector. A national unit consumption value gives an average energy demand per statistical unit, for example MWh/employee or MWh/km driven. This can be used to estimate subnational energy demand in the absence of recorded energy data below the national level. Finally, the Building Energy Rating database (which is reported quarterly by the CSO) provides details on the Irish housing stock and non-domestic building's primary heating fuels.

Least cost energy system pathways towards 100% renewable energy in Ireland by 2050.

Studies focusing on 100% renewable energy systems have emerged in recent years; however, existing studies tend to focus only on the power sector using exploratory approaches. This paper therefore undertakes a whole-system approach and explores optimal pathways towards 100% renewable energy by 2050. The analysis is carried out for Ireland, which currently has the highest share of variable renewable electricity on a synchronous power system. Large numbers of scenarios are developed using the Irish TIMES model to address uncertainties. Results show that compared to decarbonization targets, focusing on renewable penetration without considering carbon capture options is significantly less cost effective in carbon mitigation. Alternative assumptions on bioenergy imports and maximum variability in power generation lead to very different energy mixes in bioenergy and electrification levels. All pathways suggest that indigenous bioenergy needs to be fully exploited and the current annual deployment rate of renewable electricity needs a boost. Pathways relying on international bioenergy imports are slightly cheaper and faces less economic and technical challenges. However, given the large future uncertainties, it is recommended that further policy considerations be given to pathways with high electrification levels as they are more robust towards uncertainties.

Residential stock data and dataset on energy efficiency characteristics of residential building fabrics in Ireland.

These data support the research article "Improving energy savings from a residential retrofit policy: a new model to inform better retrofit decisions" - (Mac Uidhir et al., 2019) [1]. This article presents 3 data sources which are utilised in conjunction with a detailed energy system model of the residential sector to explore policy pathways for residential retrofitting. Data is collected from the Central Statistics Office (CSO) and the Sustainable Energy Authority of Ireland (SEAI). The first SEAI dataset is compiled for Ireland in compliance with the EU Energy Performance of Buildings Directive (EPBD) [2]. Data is collected using the Dwelling Energy Assessment Procedure (DEAP) [3]. DEAP is used to produce energy performance certificates known as Building Energy Ratings (BER). A BER indicates a buildings energy performance across a 15-point energy efficiency scale, rated alphabetically from A1 to G, in units of kWh/m2 year. A BER is required for new buildings and the rent or sale of existing dwellings - therefore the database has consistently grown in size since its inception in 2006. The BER database contains 735,906 records of individual dwellings. The database includes detailed building fabric information across a range of different building types, year of construction, Main/Secondary space/water heating fuels, heating system efficiency, ventilation method and structure type (Insulated concrete form, Masonry, Timber or Steel Frame). The second SEAI dataset (PWBER) contains aggregated pre and post BER information for a sample of 112,007 dwellings retrofitted during the period 2010-2015; this database contains mean energy efficiency improvement (kWh/m2 year) for a range of retrofit combinations as they apply to nine distinct building archetypes. The third CSO dataset is compiled from census data, representing the frequency of building types by year of construction.

Impacts of Inter-annual Wind and Solar Variations on the European Power System.

Weather-dependent renewable energy resources are playing a key role in decarbonizing electricity. There is a growing body of analysis on the impacts of wind and solar variability on power system operation. Existing studies tend to use a single or typical year of generation data, which overlooks the substantial year-to-year fluctuation in weather, or to only consider variation in the meteorological inputs, which overlooks the complex response of an interconnected power system. Here, we address these gaps by combining detailed continent-wide modeling of Europe's future power system with 30 years of historical weather data. The most representative single years are 1989 and 2012, but using multiple years reveals a 5-fold increase in Europe's inter-annual variability of CO2 emissions and total generation costs from 2015 to 2030. We also find that several metrics generalize to linear functions of variable renewable penetration: CO2 emissions, curtailment of renewables, wholesale prices, and total system costs.