International Energy Agency

Developing nations encounter significant challenges in accessing the necessary finance to meet climate goals. The emerging ‘Data-to-Deal’ approach is a collaborative effort by 60 specialists, which aims to addresses this by providing a flexible framework of options, tailored to individual country circumstances, aiming to enhance core functions and capabilities; it serves as a basis for concrete action, informing capacity building, technical assistance, and research. This paper argues for the mainstreaming of a holistic approach to accessing climate finance by outlining the components of the Data-to- Deal pipeline and showing the effectiveness of Data-to-Deal through the demonstration of its successful implementation in Costa Rica.

Over the last five years prior to the Glasgow Climate Pact1, 154 Parties have submitted new or updated 2030 mitigation goals in their nationally determined contributions and 76 have put forward longer-term pledges. Quantifications of the pledges before the 2021 United Nations Climate Change Conference (COP26) suggested a less than 50 per cent chance of keeping warming below 2 degrees Celsius2-5. Here we show that warming can be kept just below 2 degrees Celsius if all conditional and unconditional pledges are implemented in full and on time. Peak warming could be limited to 1.9-2.0 degrees Celsius (5%-95% range 1.4-2.8 °C) in the full implementation case-building on a probabilistic characterization of Earth system uncertainties in line with the Working Group I contribution to theSixth Assessment Report6 of the Intergovernmental Panel on Climate Change (IPCC). We retrospectively project twenty-first-century warming to show how the aggregate level of ambition changed from 2015 to 2021. Our results rely on the extrapolation of time-limited targets beyond 2030 or 2050, characteristics of the IPCC 1.5 °C Special Report (SR1.5) scenario database7 and the full implementation of pledges. More pessimistic assumptions on these factors would lead to higher temperature projections. A second, independent emissions modelling framework projected peak warming of 1.8 degrees Celsius, supporting the finding that realized pledges could limit warming to just below 2 degrees Celsius. Limiting warming not only to 'just below' but to 'well below' 2 degrees Celsius or 1.5 degrees Celsius urgently requires policies and actions to bring about steep emission reductions this decade, aligned with mid-century global net-zero CO2 emissions.

This study evaluates the effect of complete nationwide lockdown in 2020 on residential electricity demand across 13 Indian cities and the role of digitalisation using a public smart meter dataset. We undertake a data-driven approach to explore the energy impacts of work-from-home norms across five dwelling typologies. Our methodology includes climate correction, dimensionality reduction and machine learning-based clustering using Gaussian Mixture Models of daily load curves. Results show that during the lockdown, maximum daily peak demand increased by 150–200% as compared to 2018 and 2019 levels for one room-units (RM1), one bedroom-units (BR1) and two bedroom-units (BR2) which are typical for low- and middle-income families. While the upper-middle- and higher-income dwelling units (i.e., three (3BR) and more-than-three bedroom-units (M3BR)) saw night-time demand rise by almost 44% in 2020, as compared to 2018 and 2019 levels. Our results also showed that new peak demand emerged for the lockdown period for RM1, BR1 and BR2 dwelling typologies. We found that the lack of supporting socioeconomic and climatic data can restrict a comprehensive analysis of demand shocks using similar public datasets, which informed policy implications for India's digitalisation. We further emphasised improving the data quality and reliability for effective data-centric policymaking.

Abstract Energy system modelling can be used to develop internally consistent quantified scenarios. These provide key insights needed to mobilise finance, understand market development, infrastructure deployment, the associated role of institutions, and generally support improved policymaking. However, access to data is often a barrier to starting energy system modelling, 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 modelling 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 Modelling 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.

Abstract. We present results from the FAOSTAT agri-food systems emissions database, relative to 236 countries and territories and over the period 1990–2019. We find that in 2019, world-total food systems emissions were 16.5 billion metric tonnes (Gt CO2eq yr−1), corresponding to 31 % of total anthropogenic emissions. Of the agri-food systems total, global emissions within the farm gate –from crop and livestock production processes including on-farm energy use—were 7.2 Gt CO2eq yr−1; emissions from land use change, due to deforestation and peatland degradation, were 3.5 Gt CO2eq yr−1; and emissions from pre- and post-production processes –manufacturing of fertilizers, food processing, packaging, transport, retail, household consumption and food waste disposal—were 5.8 Gt CO2eq yr−1. Over the study period 1990–2019, agri-food systems emissions increased in total by 17 %, largely driven by a doubling of emissions from pre- and post-production processes. Conversely, the FAO data show that since 1990 land use emissions decreased by 25 %, while emissions within the farm gate increased only 9 %. In 2019, in terms of single GHG, pre- and post- production processes emitted the most CO2 (3.9 Gt CO2 yr−1), preceding land use change (3.3 Gt CO2 yr−1) and farm-gate (1.2 Gt CO2 yr−1) emissions. Conversely, farm-gate activities were by far the major emitter of methane (140 Mt CH4 yr−1) and of nitrous oxide (7.8 Mt N2O yr−1). Pre-and post- processes were also significant emitters of methane (49 Mt CH4 yr−1), mostly generated from the decay of solid food waste in landfills and open-dumps. The most important trend over the 30-year period since 1990 highlighted by our analysis is the increasingly important role of food-related emissions generated outside of agricultural land, in pre- and post-production processes along food supply chains, at all scales from global, regional and national, from 1990 to 2019. In fact, our data show that by 2019, food supply chains had overtaken farm-gate processes to become the largest GHG component of agri-food systems emissions in Annex I parties (2.2 Gt CO2eq yr−1). They also more than doubled in non-Annex I parties (to 3.5 Gt CO2eq yr−1), becoming larger than emissions from land-use change. By 2019 food supply chains had become the largest agri-food system component in China (1100 Mt CO2eq yr−1); USA (700 Mt CO2eq yr−1) and EU-27 (600 Mt CO2eq yr−1). This has important repercussions for food-relevant national mitigation strategies, considering that until recently these have focused mainly on reductions of non-CO2 gases within the farm gate and on CO2 mitigation from land use change. The information used in this work is available as open data at: https://zenodo.org/record/5615082 (Tubiello et al., 2021d). It is also available to users via the FAOSTAT database (FAO, 2021a), with annual updates.

Abstract Energy system modelling can be used to assess the implications of different scenarios and support improved policymaking. However, access to data is often a barrier to energy system modelling, causing delays. Therefore, this article provides data that can be used to create a simple zero order energy system model for Uruguay, 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 organizations, journal articles, and existing modelling studies. This means that the dataset can be easily updated based on the latest available information or more detailed and accurate local data. These data were also used to calibrate a simple energy system model using the Open Source Energy Modelling System (OSeMOSYS) and three stylized scenarios (Fossil Future, Least Cost and Net Zero by 2050) for 2020–2050. The assumptions used and 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.

Abstract Energy system modelling can be used to assess the implications of different scenarios and support improved policymaking. However, access to data is often a barrier to energy system modelling, causing delays. Therefore, this article provides data that can be used to create a simple zero order energy system model for Bolivia, 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 organizations, journal articles, and existing modelling studies. This means that the dataset can be easily updated based on the latest available information or more detailed and accurate local data. These data were also used to calibrate a simple energy system model using the Open Source Energy Modelling System (OSeMOSYS) and three stylized scenarios (Fossil Future, Least Cost and Net Zero by 2050) for 2020-2050. The assumptions used and 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.

Abstract Energy system modelling can be used to assess the implications of different scenarios and support improved policymaking. However, access to data is often a barrier to energy system modelling, causing delays. Therefore, this article provides data that can be used to create a simple zero order energy system model for Laos, 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 organizations, journal articles, and existing modelling studies. This means that the dataset can be easily updated based on the latest available information or more detailed and accurate local data. These data were also used to calibrate a simple energy system model using the Open Source Energy Modelling System (OSeMOSYS) and three stylized scenarios (Fossil Future, Least Cost and Net Zero by 2050) for 2020–2050. The assumptions used and 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.

Abstract Energy system modelling can be used to assess the implications of different scenarios and support improved policymaking. However, access to data is often a barrier to starting energy system modelling in developing countries, thereby causing delays. Therefore, this article provides data that can be used to create a simple zero order energy system model for Chad, 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 organizations, journal articles, and existing modelling studies. This means that the dataset can be easily updated based on the latest available information or more detailed and accurate local data. These data were also used to calibrate a simple energy system model using the Open Source Energy Modelling System (OSeMOSYS) and three stylized scenarios (Fossil Future, Least Cost and Net Zero by 2050) for 2020-2050. The assumptions used and 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.

Abstract Energy system modelling can be used to assess the implications of different scenarios and support improved policymaking. However, access to data is often a barrier to starting energy system modelling in developing countries, thereby causing delays. Therefore, this article provides data that can be used to create a simple zero order energy system model for Zambia, 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 organizations, journal articles, and existing modelling studies. This means that the dataset can be easily updated based on the latest available information or more detailed and accurate local data. These data were also used to calibrate a simple energy system model using the Open Source Energy Modelling System (OSeMOSYS) and three stylized scenarios (Fossil Future, Least Cost and Net Zero by 2050) for 2020–2050. The assumptions used and 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.