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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.
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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.
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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.
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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.
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The feasibility of large-scale biological CO2 removal to achieve stringent climate targets remains unclear. Direct Air Carbon Capture and Storage (DACCS) offers an alternative negative emissions technology (NET) option. Here we conduct the first inter-model comparison on the role of DACCS in 1.5 and 2 °C scenarios, under a variety of techno-economic assumptions. Deploying DACCS significantly reduces mitigation costs, and it complements rather than substitutes other NETs. The key factor limiting DACCS deployment is the rate at which it can be scaled up. Our scenarios' average DACCS scale-up rates of 1.5 GtCO2/yr would require considerable sorbent production and up to 300 EJ/yr of energy input by 2100. The risk of assuming that DACCS can be deployed at scale, and finding it to be subsequently unavailable, leads to a global temperature overshoot of up to 0.8 °C. DACCS should therefore be developed and deployed alongside, rather than instead of, other mitigation options.