Land, energy and water resource management and its impact on GHG emissions, electricity supply and food production- Insights from a Ugandan case study

Despite the excitement around the nexus between land, energy and water resource systems, policies enacted to govern and use these resources are still formulated in isolation, without considering the interdependencies. Using a Ugandan case study, we highlight the impact that one policy change in the energy system will have on other resource systems. We focus on deforestation, long term electricity supply planning, crop production, water consumption, land-use change and climate impacting greenhouse gas(GHG) trajectories. In this study, an open-source integrated modelling framework is used to map the ripple effects of a policy change related to reducing biomass consumption. We find that, despite the reduction in deforestation of woodlands and forests, the GHG emissions in the power sector are expected to increase in between 2040–2050, owing to higher fossil fuel usage. This policy change is also likely to increase the cost of electricity generation, which in turn affects the agricultural land types. There is an unforeseen shift from irrigated to rainfed type land due to higher electricity costs. With this integrated model setup for Uganda, we highlight the need for integrated policy planning that takes into consideration the interlinkages between the resource systems and cross propagation effects

Land, energy and water resource management and its impact on GHG emissions, electricity supply and food production- Insights from a Ugandan case study

Despite the excitement around the nexus between land, energy and water resource systems, policies enacted to govern and use these resources are still formulated in isolation, without considering the interdependencies. Using a Ugandan case study, we highlight the impact that one policy change in the energy system will have on other resource systems. We focus on deforestation, long term electricity supply planning, crop production, water consumption, land-use change and climate impacting greenhouse gas(GHG) trajectories. In this study, an open-source integrated modelling framework is used to map the ripple effects of a policy change related to reducing biomass consumption. We find that, despite the reduction in deforestation of woodlands and forests, the GHG emissions in the power sector are expected to increase in between 2040–2050, owing to higher fossil fuel usage. This policy change is also likely to increase the cost of electricity generation, which in turn affects the agricultural land types. There is an unforeseen shift from irrigated to rainfed type land due to higher electricity costs. With this integrated model setup for Uganda, we highlight the need for integrated policy planning that takes into consideration the interlinkages between the resource systems and cross propagation effects

Climate, Land, Energy and Water systems interactions – From key concepts to model implementation with OSeMOSYS

The Climate, Land, Energy and Water systems (CLEWs) approach guides the development of integrated assessments. The approach includes an analytical component that can be performed using simple accounting methods, soft-linking tools, incorporating cross-systems considerations in sectoral models, or using one modelling tool to represent CLEW systems. This paper describes how a CLEWs quantitative analysis can be performed using one single modelling tool, the Open Source Energy Modelling System (OSeMOSYS). Although OSeMOSYS was primarily developed for energy systems analysis, the tool's functionality and flexibility allow for its application to CLEWs. A step-by-step explanation of how climate, land, energy, and water systems can be represented with OSeMOSYS, complemented with the interpretation of sets, parameters, and variables in the OSeMOSYS code, is provided. A hypothetical case serves as the basis for developing a modelling exercise that exemplifies the building of a CLEWs model in OSeMOSYS. System-centred scenario analysis is performed with the integrated model example to illustrate its application. The analysis of results shows how integrated insights can be derived from the quantitative exercise in the form of conflicts, trade-offs, opportunities, and synergies. In addition to the modelling exercise, using the OSeMOSYS-CLEWs example in teaching, training and open science is explored to support knowledge transfer and advancement in the field

Selected ‘Starter Kit’ energy system modelling data for Benin (#CCG)

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 Benin, 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 two stylized scenarios (Fossil Future and Least Cost) 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

Selected ‘Starter Kit’ energy system modelling data for South Africa (#CCG)

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 South Africa, 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 two stylized scenarios (Fossil Future and Least Cost) 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

Selected ‘Starter Kit’ energy system modelling data for Burkina Faso (#CCG)

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 Burkina Faso, 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

Selected ‘Starter Kit’ energy system modelling data for Ethiopia (#CCG)

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 Ethiopia, 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 two stylized scenarios (Fossil Future and Least Cost) 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

Selected ‘Starter Kit’ energy system modelling data for Equatorial Guinea (#CCG)

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 Equatorial Guinea, 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 two stylized scenarios (Fossil Future and Least Cost) 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

Selected ‘Starter Kit’ energy system modelling data for Liberia (#CCG)

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 Liberia, 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 two stylized scenarios (Fossil Future and Least Cost) 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

Selected ‘Starter Kit’ energy system modelling data for Gambia (#CCG)

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 Gambia, 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 two stylized scenarios (Fossil Future and Least Cost) 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