The role of agriculture for achieving renewable energy-centered sustainable development objectives in rural Africa

The role of agriculture for achieving renewable energy-centered sustainable development objectives in rural Africa Giacomo Falchetta a,b,* , Adriano Vinca a , Andr´ e Troost e , Marta Tuninetti d , Gregory Ireland c , Edward Byers a , Manfred Hafner f , Ackim Zulu g a International Institute for Applied Systems Analysis (IIASA), Schloßpl. 1, 2361, Laxenburg, Austria b Centro Euro-Mediterraneo Sui Cambiamenti Climatici and RFF-CMCC European Institute on Economics and the Environment, Venice, Italy c University of Cape Town (UCT), Energy Systems Research Group, Department of Chemical Engineering, Cape Town, South Africa d Dipartimento di Ingegneria dell’Ambiente, del Territorio e delle Infrastructure, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129, Torino, Italy e TFE Africa, 152 Main Rd, Muizenberg, Cape Town, 7945, South Africa f HEAS AG, Gulmstrasse 60, 6315, Ober¨ ageri, Switzerland g School of Engineering, University of Zambia, Box, 32379, Lusaka, Zambia ARTICLE INFO Keywords: Rural development Water-energy-food-development nexus Business models Integrated development policy Productive uses of energy ABSTRACT Multi-dimensional and overlapping barriers to wellbeing severely affect many areas in rural subSaharan Africa. In the region, more than 90% of cropland is rainfed, less than one third of households have electricity, almost 60% of the population reports food insecurity, and more than 35% of the population lives below the international poverty line. Climate change impacts on vulnerable systems with limited adaptive capacity and strong population growth are increasing the magnitude of these challenges, slowing and potentially reversing development. Thus, there is a strong need for multi-sector interventions across multiple levels, from national policies, to regional and river catchment-scale planning, to local planning and investment. To implement such actions, it is key not only to assess technological solutions and their investment needs, but also to appraise their feasibility and implementation potential (from both a policy and a financial point of view). Here, we implement a modelling platform (RE4AFAGRI platform), which soft-links bottom-up process-based water and energy demand and techno-economic infrastructure assessment models (WaterCROP, M-LED, OnSSET) into a multi-node, national Nexus-extended Integrated Assessment Model (MESSAGEix-Nexus) for supply and investment assessment. The results of our analysis shed light on the role of water and energy demand in the agricultural sector for jointly affecting infrastructure and investment requirements for achieving rural sustainable development objectives. We find that scenarios with increased ambition in expanding irrigation and agricultural productivity result in improved diffusion and economic feasibility of infrastructure to provide universal energy access while supporting productive uses of energy. Moreover, we conduct business model analysis to appraise the framework conditions and micro and macro determinants that can ensure feasibility of investment and uptake of small-scale infrastructure, crucial for rural development. Altogether, our research demonstrates how integrated modelling with an explicit focus on Nexus interlinkages can represent the enabling role and the business conditions for renewable energy input in agriculture to become a leverage of rural * Corresponding author. International Institute for Applied Systems Analysis (IIASA), Schloßpl. 1, 2361, Laxenburg, Austria E-mail address: [email protected] (G. Falchetta). Contents lists available at ScienceDirect Environmental Development journal homepage: www.elsevier.com/locate/envdev https://doi.org/10.1016/j.envdev.2024.101098 Received 27 March 2024; Received in revised form 30 September 2024; Accepted 31 October 2024 Environmental Development 52 (2024) 101098 Available online 8 November 2024 2211-4645/© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ). sustainable development. In turn, important policy and investment-relevant insights can be derived. 1. Introduction Multi-dimensional and overlapping Nexus challenges affect many areas of rural sub-Saharan Africa (Falchetta et al., 2022). More than 90% of cropland is rainfed (Wiggins and Lankford, 2019), less than one third of households have electricity at home (Access to electricity), almost 60% of the population reports moderate or severe food insecurity (Food insecurity - SN), and more than 35% of people live below the international poverty line (The World Bank, 2023). Climate change impacts on vulnerable systems with limited adaptive capacity and strong population growth are increasing the magnitude of the challenge (P¨ ortner et al., 2022). As a result, there is a strong need for multi-level, multi-sector interventions (from national policies to regional and river catchment-scale planning, to local implementation and investment) that can support the achievement of sustainable development goals. For example, rural electrification can be achieved with standalone photovoltaics, island mini-grids or main grid connections, and this in turn enables a range of different services that can improve rural livelihoods, such as water pumping and irrigation, and crop processing and storage. To implement such infrastructure, it is key not only to assess technological solutions and their cost needs, but also to appraise their feasibility and context-specific (Mulugetta et al., 2022) implementation potential (from both a policy and a financial point of view). In this paper, we introduce and implement an integrated modelling framework (the RE4AFAGRI platform) aiming at assessing and planning investments along Water-Agriculture-Food-Energy interlinkages through environmental (process-based) and technoeconomic (energy and water supply) models with the aim of assessing the role of agriculture for achieving renewable energycentered sustainable development objectives, as well as the financial feasibility of implementation for such solutions. The analysis is strongly focused on the role of an explicit consideration of (productive) energy demand and access expansion in shaping the Nexus in terms of resources needs, infrastructure requirements, investment, and sustainable development objectives. Specifically, we soft-link bottom-up water and energy demand and local infrastructure assessment tools into a multi-node, national Nexus-extended Integrated Assessment Model for supply and investment assessment. Ultimately, by discussing the results of technical models in relation to those of the business models analysis, we explore the key micro and macro determinants to ensure feasibility of investment, implementation, and uptake of small-scale infrastructure operating along the WEFE (Water-Energy-Food-Environment) nexus dimensions, crucial for rural development and adaptation to changing climate conditions. This replicable, scalable, open-source framework is applied to the country-study of Zambia to demonstrate how climate impacts and water and energy needs have cascading effects that shape infrastructure and investment pathways. 2. Background and literature review Several modeling studies have been focusing on major African river basins, mostly studying the relation between agriculture activity and water management, or centralized electricity generation (e.g. hydropower) and the related water-land trade-offs (Giuliani et al., 2022; Vinca et al., 2020). Yet, few Nexus modelling frameworks have paid explicit attention to the question of local access to electricity for agricultural rural development, including the specific link between water needs, electricity demand, climate change, the local system configuration and investment costs, and the consequences for financing energy and water supply technologies (Falchetta et al., 2022). The existing analyses show that rural development and climate resilience are not possible without a transformation of the agricultural production system, which in turn relies on the provision of sustainable energy (Ramos et al., 2021; Sridharan et al., 2019). However, many of these intersections remain scarcely explored, modeled with siloed approaches, and rarely translated into technological, economic, and business model implications. Moreover, whilst previous literature has investigated some of the interlinkages between agriculture, energy access, water supply, climate change, and socio-economic development, these studies have mostly been characterized by a descriptive approach, with few infrastructure and investment planning-oriented analysis focusing on the WEFE (Water-Energy-Food-Environment) nexus. Broadly, the relevant past literature can be divided into four main strands: (i) position papers highlighting from a theoretical standpoint the importance of energy for agricultural development and recommending actions to be taken at different levels (e.g. Dubois et al. (2017); Falchetta (2021); Shirley (2021)). These studies highlighted the role of the energy input in the agricultural supply chain and the potential of the agri-food chain to support the anchor model of electricity provision (Falchetta, 2021; Kyriakarakos et al., 2020), whereby energy infrastructure expansion is made possible by the local presence of business or public facility demand sources, in contexts were the household demand only would be too low to justify investment, and hence promote rural economic development. (ii) Work focusing on quantitatively assessing and modelling the energy requirements in the context of agricultural development and energy access planning (e.g. Best (2014); Shirley et al. (2021); Nilsson et al. (2021)). This work carried out systematic assessments and employed geospatial data modelling techniques to quantify energy requirements for specific agriculture-related activities (e.g. energy for production, processing, and commercialization of agricultural products) and to achieve Nexus goals. (iii) Research assessing specific technologies or value chain along the climate-water-energy-agriculture-development Nexus (e.g. Guta et al. (2017); Best (2014); Parkinson and Hunt (2020); Bieber et al. (2018); Gupta (2019); Falchetta et al. (2023); Omoju et al. (2020)). This research analyzed the challenges and opportunities from the use of decentralized energy supply systems from a Nexus perspective. Solutions inquired include solar irrigation, agrivoltaics, and additional policies to improve agricultural productivity and profitability (iv) Large-scale integrated modelling frameworks, including applications on Zambia and the Zambezi river basin - the country-study G. Falchetta et al. Environmental Development 52 (2024) 101098 2 presented in this paper (Palazzo et al., 2024; Payet-Burin et al., 2019), which tend to focus more on the macro-scale sectoral synergies and trade-off in the use and trade of energy and water resources. Altogether, while the existing literature already demonstrated the relevance of several Nexus linkages across sectors and policy domains, current assessment models mostly focus on centralized energy systems (the central power grid) and their relations with water systems (e.g. hydropower, power plant cooling). However, these scales are not suitable for assessing the requirements for rural and decentralized systems, which are key for achieving the sustainable development goals in large parts of the Global South. In addition, Nexus models that explore access to energy and water in rural areas require high spatial resolution given the high sparsity and heterogeneity of settings affected by these issues. In this context, our paper contributes to the existing literature gap by exploring a relatively little assessed component in the Nexus modelling research, i.e. the role of agriculture in relation to the provision of energy services for achieving sustainable development objectives in rural areas. The analysis aims at explicitly elaborating around the energy access and agriculture Nexus focusing on granular provisioning systems. Moreover, it seeks at contributing to the literature integrated models to plan and estimate impacts of possible future investments while also elaborating on the challenges for the actual implementation of solutions given local financing and regulatory conditions. 3. Materials and methods The analysis presented in this paper is based on an open-source modelling framework, which is introduced as follows. First, the four models which are part of the RE4AFAGRI platform are introduced and their integration through inputs and outputs soft-linking is discussed. Specific focus is also given to the replicability and scalability of the modelling platform. Secondly, the methodology underlying the business model analysis tool is presented. Section 4then describes the Zambia country-study conducted in this paper and its implementation within the RE4AFAGRI modelling platform. This includes the development scenarios designed and implemented as part of this study. The following paragraphs describe each of these methodological and implementation steps more in detail. 3.1. The RE4AFAGRI models and platform The RE4AFAGRI platform is a multi-model, open-source, 1 documented 2 framework to analyse deficits, requirements, and optimal solutions for integrated land-water-agriculture-energy-development nexus interlinkages in developing countries (Fig. 1A) (Falchetta et al., 2022). The platform combines and soft-links four standalone, validated 3 models: (i) WaterCROP (Giordano et al., 2023; Tuninetti et al., 2015), a spatially-explicit evapotranspiration model used to estimate the crop water demand by source (rainfall plus irrigation) as a function of the daily soil moisture dynamics in the root zone and according to the potential for irrigation expansion (Allen et al., 1998) (by source, surface water or groundwater bodies) (ii) M-LED (Falchetta et al., 2021), a Multi-sectoral Latent Electricity Demand geospatial model to estimate electricity demand in communities that live in energy poverty. The platform leverages bottom-up data and energy modelling techniques to represent the potential electricity demand with high spatio-temporal and sectoral granularity, with specific attention to the implications for water-energy-agriculture-development interlinkages; (iii) OnSSET (the Open Source Spatial Electrification Tool) (Korkovelos et al., 2019; Mentis et al., 2017), a GIS based optimization model that has been developed to support electrification planning and decision making for the achievement of energy access goals in currently unserved locations; and (iv) MESSAGEix Nexus (an evolution of the NExus Solutions Tool) (Vinca et al., 2020; Awais et al., 2023), an integrated assessment model (IAM) that integrates multi-scale energy–water–land resource optimization with distributed hydrological modeling. Exploring scenarios of future socioeconomic and climatic change, it provides insight into how multi-sectoral policies, technological solutions and investments can deliver resilient, sustainable transformation pathways while avoiding counterproductive interactions among sectors. The four scientific models cover a wide range of different scales of analysis (Fig. 1B), allowing to capture the crucial multi-level Nexus dimensions which are at the core of the water-energy-development linkages assessed. Specifically, M-LED and OnSSET operate at the population settlement cluster level (and the surrounding agricultural land), while WaterCROP operates at the grid cell level (with a spatial resolution of 5 arc min or around 9 km ×9 km at the equator), and MESSAGEix-Nexus runs at the spatial scale derived from the intersection of administrative boundaries and hybrid hydrological-administrative units of a country. The results provided by the scientific models are enriched by a techno-economic tool designed to carry out localized, contextspecific assessments on the economic feasibility (from the point of view of both farmers and system developers) and the role of business models for the on-the-ground implementation of energy-water-agriculture small-scale systems, such as solar pumps, solar mills, or mini-grid powered irrigation systems managed at the community scale. It should be noted that while complementary to the RE4AFAGRI modelling platform, the techno-economic tool is not directly linked via data or model results to the platform. The main reasons are the very different scale and nature of its application: the techno-economic tool is in fact designed to perform (potential) project-specific simulations of different business models, policy and economic conditions, and cost and technology parameter to evaluate the economic feasibility and profitability of specific agro value-chain energy-powered appliances in specific contexts. It is hence a tool closer to the implementation level, while the RE4AFAGRI modelling platform operates at the location-level, and thus it is 1 https://www.leap-re.eu/wp-content/uploads/2023/03/LEAP-RE_D12.3-Integrated-platform-source-code-on-Github-as-main-V1-Not-approvedby-the-European-Commission-yet.pdf. 2 https://www.leap-re.eu/wp-content/uploads/2023/06/LEAP-RE-D12.4-User-guide-for-the-modelling-platform-on-Github.pdf. 3 https://www.leap-re.eu/wp-content/uploads/2024/01/LEAP-RE_D12.5-Joint_EU_AU_report_on_the_platform_testing_and_validation_activity_ V1.pdf. G. Falchetta et al. Environmental Development 52 (2024) 101098 3 more appropriate for the policy level. Hence, the insights from the business model analysis tool should be regarded as a complementary analysis of the challenges and consideration that needs to be considered at the implementation-scale but cannot be embedded in largescale Nexus analysis frameworks such as the RE4AFAGRI modelling platform. 3.2. Model integration within the RE4AFAGRI modelling platform Table 1 provides an input-output data linkages matrix across the four RE4AFAGRI platform models, while a detailed report of the main input data for each of the four models is provided in Table SI1 in the Appendix. As seen from the Table, WaterCrop is at the top of the soft-linked modelling chain, meaning that it does not receive direct input from the other RE4AFAGRI models (although its climate and land inputs are harmonized with those used in the other models). On the other hand, WaterCrop provides direct input into M-LED and MESSAGEix-NEXUS (affecting water pumping electricity demand and crop processing energy demand through the agricultural throughput), and it indirectly affects OnSSET. Indeed, M-LED provides direct inputs into OnSSET and MESSAGEix-NEXUS by means of sectoral electricity demand for each settlement cluster and urban-rural stratified hybrid hydrological-administrative units (BCU units). Finally, OnSSET feeds directly into MESSAGEix-NEXUS. It is worth noting that in the absence of the soft-linkages detailed in Table 1 and established as part of the RE4AFAGRI platform, several Nexus interconnections would not be represented, and the local-to-national scale implications might be lost. Specifically, MLED would not be able to provide a granular representation of the agriculture-related energy demand in the rural cluster. As demonstrated in the Results section, this share can be substantial and it can make a significant difference in determining the costFig. 1. Workflow of the RE4AFAGRI integrated modelling platform and of the multi-scale approach, (A) Schematic representation of the modelling platform and the model soft-linkage; adapted from Falchetta et al. (2022) (B) Schematic representation of the multi-scale nature of the RE4AFAGRI integrated modelling platform from population cluster-level [M-LED, OnSSET], to cropland pixels [WaterCrop], to hybrid hydrological-administrative units (BCU units) [MESSAGE-NEXUS] for the example of Zambia. G. Falchetta et al. Environmental Development 52 (2024) 101098 4 optimal type of electricity access solution in a cluster, as well as its size and investment requirement, hence significantly affecting the national electrification strategy (see the ESMAP-GEP for reference 4 ). This cascading effect is observed through OnSSET, which conventionally relies on a “flat” urban-rural tier-based approach which remains unaware of the effective productive agriculturerelated activity and consequent energy needs happening in a given cluster. In turn, consideration of agricultural energy needs also significantly affects the economic feasibility, payback time and rate of return of rural electricity supply systems, as demonstrated in the analysis carried out with the TFE techno-economic tool. Furthermore, were WaterCROP, M-LED, and OnSSET not providing their irrigation water needs, energy demand, and on-grid/off-grid electricity supply shares inputs at the urban-rural stratified BCU-unit level, then MESSAGEix-NEXUS would only rely on a simple downscaling of national energy and water statistics. This would introduce a major source of error in the calibration of current resource use and in the planning of future infrastructure. The Discussion section elaborates further on the relevance of the incorporation of the Nexus soft-linkages and the dimensions which are more significantly affected. Finally, it should be highlighted that the soft-linking and integration of the models in the RE4AFAGRI platform was tested and validated for Zambia, by involving local stakeholder discussions to help fine-tune assumptions and parameters and comparing with existing national modelling studies carried out in the country 5 (see Section 4, Implementation). An extensive technical report on the data harmonization and model interlinking can be found in HAFNER et al. 3.3. Platform replicability and scalability The RE4AFAGRI platform is fully compliant with open-source code and open-data principles, with the objective of allowing users to use, replicate, adapt, and scale to other geographies and scenarios the analysis presented in this paper. A Github repository (https:// github.com/iiasa/RE4AFAGRI_platform) hosts the source code of the modeling platform, which, in combination with the input data bundles hosted on the RE4AFAGRI Zenodo channel (https://zenodo.org/communities/leapre_re4afagri), allows to run the analysis from scratch with customized assumptions and data, or adapt it to other geographies. Moreover, the RE4AFAGRI Wiki page (https:// github.com/iiasa/RE4AFAGRI_platform/wiki) hosts the official documentation of the modelling platform, also detailing how to replicate the Zambia analysis presented in this paper and how to initialize a scaling process to another country. A set of training videos are also available in a playlist on IIASA’s YouTube channel (https://www.youtube.com/playlist?list=PLEZhFf8cpoQTM1Fol8LN8bbKJydh09Vi). Finally, the RE4AFAGRI website (https://www.re4afagri.africa/business-models) hosts the TFE techno-economic tool and its documentation. 3.4. Business models analysis: methods and assumptions As a complement to the RE4AFAGRI modelling platform (Fig. 1), a techno-economic tool was developed as part of the RE4AFAGRI project to determine the financial viability of electrifying agro-processing and irrigation activities from the perspective of the smallholder farmer 6 . The business model analysis tool calculates the payback period of a smallholder farmer’s purchase of solar water pumps and agro-processing machines. In other words, it calculates the time (in months and years) it would take the farmer to recover the initial capital expense. The model also measures financial viability in terms of net present value (NPV) and internal rate of return (IRR). The model considers a very wide set of variables as inputs (Table 2): it is sensitive to the number of hours that the equipment is operational per day and throughout the year, input costs such as electricity tariffs and maintenance costs, the price margin of the crop being sold, whether the farmer rotates between crops, and more. In parallel to the techno-economic tool, an assessment of best practice business models for standalone and mini grid electrification of smallholder agriculture was also conducted. Particular focus was placed on pay-as-you-go (PAYGO) permutations, appliance financing approaches, “KeyMaker” models and community-centered models. PAYGO and appliance financing approaches are especially useful as they address the challenge of high upfront expenses through payment plans, while the KeyMaker model serves as a way for the energy supplier to support the farmer to sell their newly processed crops in downstream markets where better prices can be achieved. Table 1 Input-output model linkages matrix. WaterCrop M-LED OnSSET MESSAGEix-NEXUS WaterCrop – – – – M-LED Irrigation water demand Potential yield increase – – – OnSSET –Sectoral electricity demand –Cost of central grid electricity MESSAGEix-NEXUS Irrigation water demand Potential yield Sectoral electricity demand Split of electricity access solutions – 4 https://electrifynow.energydata.info/. 5 https://www.leap-re.eu/wp-content/uploads/2024/01/LEAP-RE_D12.5-Joint_EU_AU_report_on_the_platform_testing_and_validation_activity_ V1.pdf. 6 https://www.re4afagri.africa/business-models G. Falchetta et al. Environmental Development 52 (2024) 101098 5 4. Modelling platform implementation 4.1. Zambia country study description and model implementation and calibration Zambia is a landlocked country in southern Africa, representing a relevant example of growing environmental pressure due to climate change (with changing rainfall patterns, increasing water scarcity, and threats to both agricultural productivity and hydropower reliability) (Kusangaya et al., 2014; Spalding-Fecher, 2018; Falchetta et al., 2019), and a growing population and economy (AfDB, 2018; Population), where it is crucial to assess and plan infrastructure, policies and investments along the water-energy-food-climate-agriculture nexus. Zambia boasts abundant water resources (Fig. 2A; Table 3, primarily sourced from the Zambezi River and its tributaries. The availability and management of water are central to the agricultural sector, which forms the backbone of Zambia’s labor source and whilst accounting for almost 60% of employment (The World Bank, 2023), it has a very limited value added (only 3% of total GDP (The World Bank, 2023)) (see Table 3). Electricity demand in Zambia: the mining industry consumes roughly 50% of electricity in Zambia and is a non-negligible part of GDP. The mining sector is covered by the electricity demand estimates and projections carried out in this paper - thus affecting the results of the analysis. Yet, it should be noted that it remains marginal in our analysis because our paper mostly focuses on the role of rural areas and agriculture. Final energy demand (which includes non-electric commodities, Fig. 3A) is dominated by the residential and commercial sector, followed by industry, public sector (non-commercial) and transport. The energy consumption in the agriculture sector is currently negligible compared to the aforementioned. Water demand in Zambia: Hydropower is the first in terms of freshwater withdrawals in Zambia, with about 1 km 3 in 2020. However, most of the water used for electricity generation is reusable downstream. Other demand sources, which are mostly waterconsumptive, are municipal, industrial and irrigation. In 2020 irrigation was still not the major source of water withdrawals (Fig. 3B), with room for increase in some basins. Electricity generation in Zambia is intricately linked with both water and agriculture: hydropower, the dominant source of electricity generation (currently producing 91% of the national consumption, at 17.6 TWh/yr. in 2022 (Zambia)), relies heavily on the country’s water resources. Variations in water availability due to climate change can thus have a cascading effect on energy production, affecting not only the power sector but also the irrigation systems crucial for agricultural activities (Calzadilla et al., 2014; Shew et al., 2020). Previous research demonstrated that climate change impacts imply a reduction in capacity factors and reliability of hydroelectric power in the Zambezi river basin, as well increasing sectoral competition for water resources (Spalding-Fecher, 2018; Cervigni et al., 2015). With regards to energy access, currently Zambia has a national electricity access rate of 34% and a governmental target of full electrification by 2030 (Zambia Power Africa, 2022). The low population density in the country (28 people/km 2 ) contributes to the challenge of extending the national grid, especially in rural areas, where the bulk of the deficit communities are located. In addition, as discussed in Mfune and Boon (2008), Zambia is currently affected by a limited uptake of renewable energy technologies in rural areas due to inadequate policy provision and implementation, lack of awareness among rural households about the benefits of renewable energy, the high capital costs of technology and the undeveloped nature of renewable energy markets. The RE4AFAGRI models are calibrated, as documented in Hafner et al. (2023), using recent energy and water statistics (See Tables SI1A-D for an account of the input and calibration data to each model), as well as with inputs from stakeholder groups to define the value of a set of technical parameters and cost assumptions. Finally, it should be noted that differently from previous developments of the model and from other Nexus integrated models applied to the Zambezi region, the version included in this framework does not have a dynamic representation of the river flow and storage. This simplifies the model but also means that we do not consider upstream-downstream responses of water withdrawals. We justify this choice being our focus mostly on rural off-grid power generation, rather than on hydropower management and centralized electricity generation. Irrespective of this limitation, it should be noted that transnational water transfers within the same basin are considered boundary conditions. Table 2 Key input parameters to the techno-economic tool. Parameter Unit Operational hours per day of the equipment hours/day Operational months per year of the equipment months/year Grid tariff (if the equipment is connected to the main grid) USD/kWh Mini grid tariff (if the equipment is connected to a mini grid) USD/kWh Current price per liter of diesel USD/l Distance from farm to market and back km Fuel consumption of the vehicle transporting crops to market l/km Estimated spend on equipment maintenance per year USD/year Monthly salary of the equipment operator USD/month Upfront cost of the equipment USD Power rating of the water pump/agro-processing machine kW Maximum possible throughput of the agro-processing machine kg/hour Price of unirrigated or unprocessed crop USD/kg Price of irrigated or processed crop USD/kg G. Falchetta et al. Environmental Development 52 (2024) 101098 6 4.2. Scenarios, development pathways, and their implementation In the implementation presented in this paper, we developed three scenarios based both on the SSP-RCP framework (O’Neill et al., 2017; van Vuuren et al., 2011) used in the Integrated Assessment Modelling community (Figure SI3) to evaluate future pathways and impacts in relation to energy and climate change, and on a scenario design process which is specific to the rural developing realities addressed by the framework implemented in this paper. The SSP-RCP logic determines the future narratives and trends in fundamental socio-economic elements (population, GPD, urbanization), as well as the impacts of climate change on land, water, and energy systems. Figure SI4 illustrates the projected socio-economic and climate change pathways in Zambia under the SSP-RCP framework. The additional scenario dimensions are instead specific to the development policies which are most relevant for the WEFE Nexus assessed with the RE4AFAGRI project. The design of the latter component was the result of a participatory workshop and in-depth discussions with stakeholders (see SI Appendix for a Table of involved stakeholders). In this context, Table 4 illustrates the three scenarios assessed in this paper. The baseline scenario represents an extrapolation of recent trends into the future, and it serves to highlight potential challenges in absence of changes in trends (e.g., persistent gaps in energy, water access and adequate nutrition). In the improved access scenario, some efforts are made to improve the quality of living in the case-study country, by increasing energy, water, and sanitation access so that the access gap estimated in the baseline (percentage of population remaining without access) is at least halved by 2030. A food nutrition target also aims at ensuring domestic food production by improving crop yields through irrigation. Finally, the ambitious development scenario includes ambitious and ideal targets of universal access to electricity, water, and Fig. 2. Maps of selected water, energy, and agriculture statistics in Zambia. Data sources: river network (Lehner et al., 2011); power plants (Byers et al., 2018); annual agricultural harvested area (rainfed and irrigated areas) for year 2010 (International Food Policy Research Institute). Table 3 Selected statistics for Zambia WEFE Nexus. Dimension Variable Value Source Economy Share of GDP from agriculture 3 (% of total) The World Bank (2023) Economy Share of employment from agriculture 60 (% of total) The World Bank (2023) Economy People classified as “extremely poor”48 (% of total) (Highlights of The 2022 Poverty Assessment In Zambia) Energy Electricity demand 17.6 TWh/yr. (Zambia) Energy Share of population with access to electricity 34 (% of total) (Access to electricity) Socio-demographics Population 21 (million people) The World Bank (2023) Socio-demographics Rural population share 54.3 (% of total) The World Bank (2023) Water Hydropower production 91 (% of total) (Zambia) Water Share of irrigated cropland 4 (% of total) AQUASTAT (2005) Water Share of people with access to safe water 64 (% of total) (Water and Sanitation and Hygiene ) G. Falchetta et al. Environmental Development 52 (2024) 101098 7 Table 4 Policy scenarios modeled. 12 Scenario Socioeconomic Climate change Agriculture and food targets Electricity access targets Additional development targets and policies Baseline SSP2 RCP 7.0 project historical trend of production Current policies Current policies Improved access SSP2 RCP 7.0 increasing water supply to meet domestic food crops production demand in 2030 7 halving the gap by 2030 water access & sanitation: halving the gap in 2030 Ambitious development SSP2 RCP 7.0 increasing water supply to improve yields and meet future food crop production demand consistently with the EAT Lancet diet in 2030 8 ; Renewable electricity share =100% +climate constraints universal access universal water access & sanitation G. Falchetta et al. Environmental Development 52 (2024) 101098 8 sanitation by 2030, and domestic agriculture production improving to meet decent living nutrition standards (EAT Lancet diet (Willett et al., 2019)). In addition, measures to guarantee 100% renewable electricity generation are in place. This scenario includes different levels of ambitions in improving the production and access to electricity, water infrastructure and water for agriculture, with different grades of investment requirements or secondary impacts on natural resources (e.g., coal or water withdrawals). Additional scenarios could be run up to assess the sensitivity to specific model parameters (e.g., technology costs) or to address specific questions (e.g., intensification vs extensification of agriculture, or achieving targets in 2030 and or 2050 etc.). This scenario choice enables comparison of historically observed development trends with goals set by the Sustainable Development Goals. Different levels of ambition and speed of change can provide policy makers with an estimate of the financial requirements and combined with development. Fig. 4 compares the level of ambition of the scenarios across the WEFE development objectives set. 4.3. Statistical analysis of results While most of the figures presented in the Results section are based on the direct output from the models, we conduct an ex-post statistical analysis on the M-LED and OnSSET results to appraise the relevance of agriculture-related demand for electricity access pathway, a key issue given our main research question. To achieve this aim, we combine cluster-level energy demand estimates from M-LED in each scenario, we calculate variables indicating the total cluster demand (across all sectors, TOTDEM) and the proportion of the local demand that is related to agricultural sectors (AGRISHARE), and we combine such statistics with cluster-level OnSSET results indicating the cost-optimal electricity access solution at each year and in each scenario (ELYTECH). Based on the joined model results dataset (which jointly covers all scenarios results), we estimate the following multinomial logistic regression models, which allow estimating the association of a set of predictor variables X with the probability of a given class i of the categorical outcome variable ELYTECH with respect to a base class j of the that variable. Specifically, separately for years 2030 and 2060 we estimate the following: ELYTECHist =TOTDEMist +AGRISHAREist +SCENs+ϵist where i is each rural cluster covered by the M-LED and ONSSET models, s is each of the three scenarios assessed in our study, and t is each of years 2030 and 2060 (medium and long-run model horizons). ϵist represents the residual error term. 5. Results 5.1. Granular estimates of water and energy needs for agriculture The WaterCrop model for Zambia projects (Fig. 5, bars on the right) that in 2030, 141 [95–187, scenarios range] MCM (million cubic meters) of water will be required to achieve the irrigation expansion goals set by the three scenarios assessed, growing to 425 [239–610] MCM by 2050. Thanks to the input of irrigation water, WaterCrop estimates that yields will increase considerably. For instance, under the assumption of availability of other inputs, maize yield may reach 8.1 ton/ha (+242%) by 2040, hence getting closer to the typical yield values obtained in temperate climates. Rice may see a significant increase reaching an average yield of 6.8 ton/ha (+423%) by 2040. In response to the projected water demand and agricultural production growth, the agriculture-related electricity demand in rural Fig. 3. Final energy demand and water withdrawals sectoral splits in Zambia in 2020. G. Falchetta et al. Environmental Development 52 (2024) 101098 9 underserved customers in hard-to-reach areas with equipment that is in line with the ability-to-pay of customers. Finally, the reduction or removal of import duties and value added tax on irrigation and agro-processing equipment lowers the overall cost base. A lever additional to end-user cost reduction is consumer finance schemes (such as pay-as-you-go or lease-to-own models) whereby customers pay for their equipment over time. Digital technologies and systems such as mobile money enable suppliers to transact seamlessly with customers and policymakers would be well advised to create favorable conditions for development of the mobile money ecosystem, such as the reduction of taxes on mobile money payments. Agricultural extension programs that support smallholder farmers to sell their crops in downstream markets (where crop prices are higher), e.g. major towns, is a useful tool that policymakers can use in increasing farmers’ crop prices and in turn the financial viability of the solar water pump irrigating the same crops. 6.3. Limitations and future research Altogether, our research demonstrates how national-scale integrated modelling with an explicit focus on Nexus interlinkages allows for assessing locally relevant productive demand sources and investment needs, and their implications for sustainable development. Despite the substantial effort to integrate the interactions among sectors and to represent the financial constraints, besides the resource requirements and technological characterization, limitations remain. For instance, future research could investigate the relevance of clean cooking goals and their relevance for energy demand and rural solutions business models. Moreover, a better characterization of the uncertainty in the parameter space of both macro-trends (e.g. socio-economic transformations, climate change scenarios) and local market conditions (e.g. technology prices, crop prices, climate-related risk for agricultural production) would strengthen the policy relevance of the assessment carried out in this paper. Finally, the framework implemented in this paper does not have a dynamic representation of the river flow and storage. Hence, upstream-downstream and international responses of water withdrawal are not factored in. We justify this choice being our focus mostly on rural off-grid power generation, rather than on hydropower management and centralized electricity generation. Nonetheless, transnational water transfers within the same basin are considered boundary conditions. CRediT authorship contribution statement Giacomo Falchetta: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Adriano Vinca: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation. Andr´ e Troost: Writing – review & editing, Writing – original draft, Software, Methodology, Investigation, Formal analysis, Conceptualization. Marta Tuninetti: Writing – review & editing, Validation, Software, Methodology, Investigation, Formal analysis, Data curation. Gregory Ireland: Writing – review & editing, Validation, Software, Methodology, Investigation, Formal analysis, Data curation. Edward Byers: Writing – review & editing, Writing – original draft, Supervision, Project administration, Methodology. Manfred Hafner: Writing – review & editing, Supervision, Resources, Project administration, Funding acquisition. Ackim Zulu: Writing – review & editing, Writing – original draft, Validation, Supervision. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments Financial support from the European Commission H2020 funded project LEAP-RE (Long-Term Joint EU-AU Research and Innovation Partnership on Renewable Energy), grant number 963530 is gratefully acknowledged. Vittorio Giordano is acknowledged for his help on the WaterCROP elaborations. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.envdev.2024.101098. Data availability Data is publicly available, as discussed in the data availability statement in the paper. 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