4th UN Data Innovation Lab, University of Nairobi

4th UN Data Innovation Lab, University of Nairobi
4th UN Data Innovation Lab Workshop University of Nairobi Joanna Felix Development Policy & Analysis Division Department of Economic and Social Affairs 14 March 2017 4th UN Data Innovation Lab, University of Nairobi

The CLEWs Mauritius Case Study
1. The food-energy-water nexus and sustainable development 2. The CLEWS modelling approach 3. CLEWS case study of Mauritius 4. Hands-on exercises with CLEWS 5. Modelling tools to uncover hidden relations, quantify relations and confirm suspicions 14 March 2017 4th UN Data Innovation Lab, University of Nairobi

1. The Food-Energy-Water NEXUS and Sustainable Development
14 March 2017 4th UN Data Innovation Lab, University of Nairobi

Sustainable development: food-energy-water nexus & CLEWs
Direct, inner circle, and indirect, outer circle, links with the 2030 Agenda and the SDGs Rationale: the objective is to illustrate the links among these 4 components (water, land energy and climate). They are certainly featured in the SDGs but beyond this, there are natural/structural interrelationships among these components. There are also important indirect linkages to other issues. The food-energy-water NEXUS directly relates to No Poverty (SDG-1), Zero Hunger (SDG-2), Clean Water and Sanitation (SDG-2), Life on Land (SDG-15), Renewable Energy (SDG-7), and Climate Action (SDG-13), among others. It also relates to others such as: SDG-8, SDG-9, SDG-12 The NEXUS based models can be used assess the potential and the implications of all three components of SE4ALL. The CLEWS framework can be used to assess the following issues: i. The sustainability of infrastructure development (e.g. water and energy) ii. Broader impact of activities along production value chains. iii. To illustrate how climate change impacts different sectors of the economy. Both the NEXUS and CLEWS approaches can be employed in both planning and capacity building efforts 14 March 2017 4th UN Data Innovation Lab, University of Nairobi

Development challenges
Undernourishment: 900 m. people 108 countries with 5+ % deprived population 28 countries with 20+ % deprived population No access to electricity: 1.1 b. people 50 countries where 33+% of deprived population No modern fuels , cooking or heating: 3 b. people No safe water: 900 m. people 36 countries where 20+% of deprived population No adequate sanitation: 2.6 b. people 66 countries where 20+% of deprived population Risk of climate change & pollution health & environment hazards Risks are higher where development needs are bigger Development challenges Note: this is a reference slide, not to be presented thoroughly, but to underscore underlying food-energy water nexus Highlight the global and the policy country dimension of global goals. Country figures show the range of deprivation in countries. The bottom billion people in developing countries lack subsistence-level access to food, energy and water goods and services; the lower middle-income people finds themselves spending increasingly larger proportions of their incomes to gain such an access; the upper-middle and high-income people overuse them with a substantial share wasted. Meeting global development goals represent significant challenges for many countries. These countries must exert particularly strong efforts to implement effective sustainable development policies The of the University of Nortre Dame has calculated indexes of exposure to climate change and correlate them with income

Inter-linkages: Water use for agriculture
Production of food accounts for the largest component of the human water footprint. About 70% of freshwater withdrawals worldwide are for agriculture Can be as high as 90% in some low income countries Lower in most higher income countries Efficient irrigation, combined with optimum delivery of fertilizers and crop- enhancement and –protection products holds significant promise for increased productivity and water efficiency while reducing runoff. As a result, farmers can not only produce more food, but also become more efficient stewards of their land, protecting against rain runoff, soil erosion, water or heat stress on plant, flooding, and desertification or arable land. Policy reform (water pricing, water rights, and subsidies etc.) and technology deployment are vital. Free/under- priced inputs leads to wasteful practices.

Inter-linkages: Water for energy supply
Approximately 20% of freshwater withdrawals are for industrial purposes Cooling thermal processes in the power and manufacturing sectors Primary fuel extraction Fuel refining and conversion Emission control Biofuel/biomass production Rationale: highlight the importance of water in the energy sector Water is used for cooling in thermal and nuclear power plants used (not consumed) in hydro power plants Heavily used for fuel extraction, fuel refining Emissions control technology For growing fuel crops like jatropha, sugarcane, etc.

Inter-linkages: Energy for water supply
The water supply chain is energy intensive (approx. 7% of global electricity use) Supply: Surface water: kWh per million litres for transportation depending on distance and change in elevation Groundwater: Varies with depth (e.g. 140 kWh per million litre at 35 m and 530 kWh per million litre at 120 m) Treatment: 26 kWh per million litre for high quality groundwater kWh per million litre for brackish groundwater desalination kwh per million litre for seawater desalination Distribution: Average of 290 kWh per million litre, but varies widely depending on distance and change in elevation Water needs to be brought from the source to where it is needed. While some water supply systems are gravity driven, many need pumping to bring the water to consumption centres. The amount of energy required will vary widely from system to system; mainly determined by the distances the water needs to be conveyed and the topography (i.e. change in elevation). For groundwater the energy requirement is directly related to the depth of the water table. As water tables drop, the amount of energy needed to lift it to the surface will increase accordingly. Treatment of water (before and after consumption) is dependent on the treatment technology, the quality of the water source, and the desired quality of the water available for consumption. Distribution from the treatment facilities to the end-user also requires pumping. As water demand increases and we need to access lower quality sources of water (such as sea water or brackish water) making water supply more energy intensive, although technological improvements (shift to RO and improvements in RO) are making treatment more energy efficient As the world population is increasingly congregating in cities, the demand for water will become increasingly concentrated and increase the need for water transportation Sources: Cambridge Energy Research Associates, NRDC and Pacific Institute, “Energy down the drain: The hidden costs of California’s water supply”, 2004, US DOE, “Energy Demand on Water Resources: Report to Congress on the Interdependence of Energy and Water”, 2006 EPRI, “Water and sustainability (Vol 4): US Electricity consumption for water supply and treatment – The next half century, 2000

Inter-linkages: Land use for energy
A growing share of land is dedicated to supplying energy Bioenergy accounts for little more than 10% (60 EJ) of world total primary energy supply On average 11% of coarse grains, 11% of oil seeds and 21% of sugar cane was used for biofuel production over the period 495 TWh of electricity was produced from biomass in 2014 or 2.0% of world electricity generation. Rationale: highlight the important of land for energy Growing crops for producing biofuel is leaving a big footprint on land use Land use is an important factor in large scale PV power plants and wind turbines. There are some innovative ways to increase efficiency on the use of land for solar energy, e.g. placing the panels on top of canals and on land with scatter vegetation

Inter-linkages: Energy for agriculture and food
Energy is required to power agricultural machinery for field preparation, crop harvesting, drying and processing. Direct energy use accounts for roughly 2.1% of total final energy demand Energy is required to produce fertilizers, pesticides and other agricultural inputs. Indirect energy use in agriculture is about 1.2% of total final energy demand In addition, energy is needed to process, transport, store food, package and market food and food products Rationale: energy is vital for agriculture There are strong inter-linkages between food production/land-use, energy, and water Policy decision making needs to take these inter-linkages into account An integrated approach that simultaneously takes them into account is not only desirable but also advisable Inter-linkages and integrated narratives are useful But numbers are needed, suggesting the use of quantitative models *Energy (primarily electricity and fossil fuels) is actually used throughout the life cycle of crops: from transportation of seeds to the farm, all the way to transporting agricultural products to markets for consumption

2. The CLEWs Modelling Approach

quantifying the FEW-NEXUS
CLEWS By adding climate allows assessing policy robustness to climate change quantifying the FEW-NEXUS Energy Model Land-use Model Water Model Energy for water processing and treatment Energy for water pumping Energy for desalination Water available for hydropower Water for power plant cooling Water for (bio-)fuel processing Energy for fertilizer production Energy required for field preparation and harvest Biomass for biofuel production and other energy uses Climate GHG emissions Quantifies the Food-Energy-Water NEXUS The diagram shows the main components of the CLEWS analysis and modelling. Note: For a non-technical crowd the link between GHG emissions and climate needs to be conveyed in a subtle manner: CLEWS provides output indicators relevant to climate change such as GHG emissions, changes in area covered by forests, among others But these outputs are not run through a climate change model CLEWS uses expected changes in relevant factors for water, land-use, and energy systems; examples include precipitation and temperature. Precipitation, temperature Water for biofuel crops (rain -fed and irrigated) Water needs for food, feed and fibre crops (rain-fed and irrigated)

Modelling tools used Required model inputs include:
Definition of all catchment areas Real climatic data: Rainfall, min & max temperature, humidity .... All main rivers & reservoirs plus stream flow data and reservoirs levels Modelling of existing canals / distribution systems Using GIS: Land cover classes to calculate evapotranspiration Water demand data (urban and agricultural) according to national statistics and population density Surface water bodies and groundwater are taken into account. Spatially based modelling allows for water demand at each geographical point to be defined. Similarly, water resources and precipitation at each point are defined. By having water outputs (i.e. water consumed, losses through evapotranspiration etc) and water inputs (i.e. via precipitation, surface and ground water bodies) modelled in each point, the model can strike a mass balance for all relevant processes. GIS: Geographical Information Systems Temperature and humidity affects evapotranspiration, which in turn affects irrigated water demand. Higher temperatures and low humidity levels result to increased losses through evapotranspiration, which means that crops require higher water inputs. Flow data and reservoir levels are especially important for determining at what capacity hydropower plants can operate. Canals and distribution systems determine accessibility of each point to water. The type of soil affects how and at what speed water can move through the ground.

Energy system modelling
Used to represent physical flows, capacities and energy balances through an energy system Accounting models: Can be used to assess the impact of predetermined pathways for development (LEAP, MAED) Simulation models: Represent decisions of actors within the system Potential for replacing existing technologies with low-carbon, more efficient or cost effective alternatives. Optimization models: Can be used to optimize a specific system; cost- minimization modelling (OSeMOSYS, MESSAGE, MARKAL) Technology learning rates, resource availability, technical limitations, vintage of infrastructures, penetration rates, environmental criteria, costs etc. directly affect the optimal system design. Make connection with module 3, saying that this will be covered in more detail separately. Energy system models can generally be categorized into bottom-up technoeconomic models and top-down macroeconomic models. The former employ a high degree of detail in terms of technologies but cannot provide any insights on net impacts across the economy. The latter look into aggregated sector-specific energy demand and supply and assess effects on the entire economy but are not suitable for analysing potential technology deployment, due to insufficient detail in this regard. This slide only refers to bottom-up models, giving the three sub-categories of simulation, optimization and accounting.

Energy system model: Critical questions
What are the investment requirements in generation and network infrastructure and their timing to meet electricity demand and when? What is the impact on energy security? What is the system cost for expanding the system to offer a certain level of modern energy services? What technologies and fuels achieve the least-cost and most reliable energy mix? What are the associated impacts on land-use? E.g. from growing biofuels or from large-scale solar PV parks What are the associated water requirements for a specific energy mix? E.g. water for cooling, hydropower, irrigation of biofuels Which pollutants are emitted and at what level? Note: If workshop includes the Energy Systems Module, this slide can be skipped. Investments in energy infrastructure have long lifetimes, generally lasting more than a couple of decades. Due to this, investment decisions need to be made taking into consideration the technoeconomic environment in the future, so as to minimize financial risk. >>This can be a starting sentence for the first two bullet points. Energy acts as a driver of economic development, especially in developing countries, but also leads to impacts on land-use, water-use and, depending on the technologies used, can release pollutants in the atmosphere, on land or in water bodies. >>This can be a starting sentence for the last three bullet points.

Agro-ecological modelling
Geospatially based analysis of agricultural output to assess What is the potential yield of a range of crops in each region? What are the water requirements for each crop in each modelled sector? How do different climate scenarios affect crop yield? Quantification of input requirements to achieve a certain level of yield. Agriculture covers one of humanity’s key necessities: access to food. At the same time, crops can be grown for energy purposes, for instance for use as biofuels in transport. A greater agricultural yield could potentially improve the trade balance of a country; be it for food or for energy. However, intensive agricultural practices can add stress to the natural resources of the region. Land may need to be converted for agricultural purposes, additional water may be needed for irrigation, energy is needed for water pumping and operation of machinery, while fertilizers and pesticides may be required to improve the yield and protect it from diseases/pests. Agricultural yield can be affected by factors out of our control (e.g. precipitation, temperature, extreme weather events). Since agriculture is such an important aspect of our economy, it has to be carefully managed. >> Then make a connection of this narrative with the questions above.

Modelling tools used AEZ - The Land-Use Model ... Input: Output:
Climatic Data Detailed soil map and data from soil profiles Slopes and marginal land GIS data for landcover Irrigated areas Output: Grid map showing optimal crops, potential water use, and potential yield, plus crop calendar A database that contains information for a large number of climate scenarios regarding: crop yield of different crops, water demand, suitability of land for each crop etc. The GAEZ database provides the agronomic backbone for various applications including the quantification of land productivity. Results are commonly aggregated for current major land use/cover patterns and by administrative units, land protection status, or broad classes reflecting infrastructure availability and market access conditions. Land and water resources, including soil resources, terrain resources, land cover, protected areas and selected socio economic and demographic data; Agro-climatic resources, including a variety of climatic indicators; Suitability and potential yields for up to 280 crops/land utilization types under alternative input and management levels for historical, current and future climate conditions; Downscaled actual yields and production of main crop commodities, and Yield and production gaps, in terms of ratios and differences between actual yield and production and potentials for main crops.

Climate representation
Defines and is defined by the major assumptions in the land-use, water management and energy systems models. Apparent need for consistency- e.g. greenhouse gas emission limits vs long-term estimates for air temperature and precipitation. Accounting of GHG emissions Temperature and precipitation affecting agricultural production Solar insolation affecting PV and CSP generation as well Precipitation affecting hydropower and water irrigation demand; quantifying seasonality of availability Interlinkages between effects across the sectors are highlighted. In CLEWs work, no actual climate modelling takes place, but the different components of the methodology (land modelling, water modelling, energy modelling) get their inputs from existing climate models that exist in literature. These provide information such as temperature, precipitation patterns etc, which are required in the aforementioned models. We need to be consistent in the scenarios being used – for instance if we are assuming a 2oC scenario temperature increase, then the emissions from our energy system model need to be consistent. Same applies for precipitation patterns in the land and water models. Energy system model can act as an accounting tool for emissions or optimize based on an emission limit – thus emissions can be an input or an output of the exercise.

Implementing a CLEWS Assessment
Systems profiling Current state and historical trends Main stress points Sectoral policies, plans, strategies Pre-Nexus assessment Inter-linkages between sectors Pressure points within and between sectors Focus on the nexus analysis Data availability Model development Model development – sector models to fully integrated models Model calibration Soft-linking of models inputs and/or outputs in the case of sector models Scenario design & development

Implementing a CLEWS Assessment cont’d
Analysis Analysis and interpretation of results Revise inputs / assumptions Conduct additional model runs (iterations) Inform policy making Report on the quantification of the impacts of sectoral interactions Suggestion of strategies and pathways towards sustainability

Applicability The model architecture has been designed to serve planners and decision makers in developing countries. Once properly calibrated, a national or regional CLEWS model calculates the resource and service requirements to meet socio-economic goals — such as the national SDG priorities — in a growing economy. Allows simulations of interactions within the CLEW system to meet energy, water and food related service demands. Constraints imposed by the physical and economic environments are taken into consideration, for instance: Arable land availability and agricultural production Energy resources (renewable and exhaustible) within the country Freshwater availability Environmental constraints Access to capital Energy and/or food security 14 March 2017 4th UN Data Innovation Lab, University of Nairobi

Possible CLEWS utilization
The model architecture has been designed to serve planners and decision makers in developing countries. Decision making: Assist decision and policy makers in assessing options in terms of their likely effects on the broad CLEW system and transparently evaluate the trade-offs associated with different options. Policy assessments: Ensure that policies are as cost-effective as possible. If multiple objectives can be achieved by a comprehensive policy, it may advance development more effectively than policies focused separately on single objectives. Facilitating policy harmonization and integration: Contradictory policies, e.g. electricity subsidies that accelerate aquifer depletion, which in turns leads to greater electricity use and subsidy requirements. A CLEW tool would help identify potentially conflicting policies. Technology assessments: Some technology options can affect multiple resources, e.g. a rapid expansion and deployment of solar and wind based electricity generation (instead of fossil generation) could reduce GHG emissions, local pollution and cooling water requirements, improve energy security and reduce exposure to volatile fossil fuel markets. A CLEW tool should allow a more inclusive assessment of technological options. Scenario development: Elaboration of consistent scenarios of possible socioeconomic development trajectories with the purpose of identifying future development opportunities as well as understanding the implications of different policies. 14 March 2017 4th UN Data Innovation Lab, University of Nairobi

3. CLEWS Mauritius case study

Mauritius Main revenue has been tourism and sugar exports Expiration of EU agreement and collapse of revenue from the latter. Diversification away from sugar cane to food crops and vegetables Sugar cane production and refining – staple industry Bagasse from refining – cogeneration of heat and electricity Reduction in sugar production led to lower electricity generation from bagasse Consequent increase in fuel imports – coincided with increase in international fuel prices Drainage of export revenues and higher carbon emissions Irrigation requirements higher for food crops-vegetables than for sugar cane Increased water demand 14 March 2017 4th UN Data Innovation Lab, University of Nairobi

Policy response Aim: Introduce policies that would utilize its domestic sources and substitute energy imports while alleviating water stress A target (in percent of generation) for renewable electricity generation in the national electricity mix Reduction of energy import dependency of fuel oil and coal but also to curb carbon emissions A fuel standard mandating the blending of ethanol (2nd generation) into gasoline Improve income prospects for sugar growers at lower water use, displace imported liquid fuels and improve self-sufficiency in fuel supply - energy security Are these sustainable policies? Why? 14 March 2017 4th UN Data Innovation Lab, University of Nairobi

Impact of shifting two major sugar refineries to produce 2nd generation ethanol
Figure 2: THE IMPACT OF TRANSFORMING TWO SUGAR PROCESSING PLANTS TO PRODUCE 2nd GENERATION ETHANOL (PROJECTED FOR 2020) Reduced fuel imports Reduced greenhouse gas emissions Reduced expenditures The import dependence decreases. Gasoline imports are reduced as ethanol partly replaces gasoline use in the transport fleet. Some of the bagasse is now used for ethanol production instead of electricity generation. This needs to be compensated by increased in coal and oil product imports. An overall reduction of greenhouse gas emissions occurs. Emissions are reduced in the transport sector and through reduced gasoline refining as gasoline is replaced by ethanol. The increased use of coal and oil (instead of bagasse) for electricity generation results in additional emissions. Ethanol production is economically beneficial. As some of the sugar is converted to ethanol, there are reduced expenditures for sugar refining and gasoline imports. This outweighs the costs associated with the ethanol production, the increases in oil and coal imports and the reduced sugar export earnings. Reduction of sugar exports Oil & coal prod. Oil Coal Gasoline Electricity generation Ethanol prod. Baseline Oil imports Sugar prod. [1000 GJ] [ton CO2eq] Transportation Gasoline imports [1000 US$ - Real 2005] Gasoline refining

4. Hands-on exercises with CLEWS 14 March 2017 4th UN Data Innovation Lab, University of Nairobi

Uncovering effects: Exploring policy scenarios and trade-offs using the CLEWs visualization tool Task 1: The Government is considering the introduction of a renewable electricity target (wind, solar, hydro and biomass). Task 2: The Government is considering the introduction of a renewable fuel target which would mandate the blending of ethanol into all gasoline sold in the country. This could be produced from sugarcane at sugar mills. Use the visualization tool to explore possible scenarios: Why do you think the Government might want to pursue this policy? What costs and detrimental impacts would you expect from this policy? Could any of the detrimental impacts be addressed or mitigated through the design of the policy mechanisms? 14 March 2017 4th UN Data Innovation Lab, University of Nairobi

Baseline scenario Consistent with National Plans Assumes that sugar exporters gradually lose market share to lower- cost producers but are able to find sufficient markets to maintain production levels. Water supply relies mainly on surface and groundwater resources. Groundwater is limited by concerns of potential saline intrusion. Shortfall will have to be met by desalination Energy assumptions use national investment plan- coal fired generation to meet future demand. Weather patterns were assumed to follow historical trends 14 March 2017 4th UN Data Innovation Lab, University of Nairobi

5. Modelling tools to uncover hidden relations, quantify relations and confirm suspicions 14 March 2017 4th UN Data Innovation Lab, University of Nairobi

Inter-linkages in Mauritius 14 March 2017 4th UN Data Innovation Lab, University of Nairobi

Overall water withdrawals in different integrated CLEW scenarios
Water withdrawals under climate change scenarios: Water withdrawals for domestic purposes and especially irrigation increase strongly under projected climate change scenarios. To compensate for reduced precipitation, irrigation has to be expanded to previously rain-fed sugar plantations & farms resulting in higher withdrawals of surface and ground water This affects future land use option (towards less water intensive crops) and increases energy demand for pumping and desalination Reference scenario 1st generation methanol production under a ‘moderate’ climate change scenario 2nd generation methanol production under a ‘moderate’ climate change scenario 2nd generation methanol production under a ‘worst’ climate change scenario 14 March 2017 4th UN Data Innovation Lab, University of Nairobi

Overall electricity demand in different CLEW scenarios
Increasing electricity demand for different biofuel production technologies: Energy demand increase in potential climate change scenarios – the amount of energy increase depends on the water intensity of production options: While 1st generation ethanol production is more effective in the beginning – its overall energy balance turns unfavourable against 2nd generation ethanol production in the case of reduced water availability. Reference scenario 1st generation methanol production 1st generation methanol production under a ‘worst case’ climate change scenario 2nd generation methanol production under a ‘worst’ climate change scenario 14 March 2017 4th UN Data Innovation Lab, University of Nairobi

Sustainable development and the food-energy-water nexus
Agriculture is key for “Zero Hunger” but is also highly dependent on fresh water availability and affordable energy for irrigation, as well as gender equity, climate change etc. Fossil energy use and land-use change are key contributors to climate change which may adversely affect food security, fresh water supply, good health and well-being. Access to affordable energy services is needed for poverty eradication

Data sources Official country data Water: Surface water, groundwater sources and aquifers, water quality, water demand, water supply infrastructure, wastewater treatment Climate: Historical data, precipitation, temperature, weather-related events Land use: Land-use maps, urbanization prospects, land-use change and projections, historic forest cover maps, firewood demand, agricultural land use, development plans of agriculture sector, land irrigation, etc. Energy data: electrification/expansion projects, technical description of new electricity investment projects (investment costs, commissioning dates), location and area, cooling systems of existing and planned thermal, cooling water requirements (source, water consumption), data on reservoirs, etc. Open data sources Satellite data for water, climate, land-use (GAEZ) International Institute of Applied Systems Analysis (IIASA) IRENA International Energy Agency (IEA) S4All (Sustainable Energy for All; Energy Data (World Bank) Private sources 14 March 2017 4th UN Data Innovation Lab, University of Nairobi

Closing Remarks CLEWS studies cannot assess all relevant issues: Quantification and valuation of ecosystem services to assess the impact of cropping practice on loss of biodiversity. The expansion of agriculture into natural habitats and the adoption of monoculture CLEWS type of models help integrated planning at the local and national levels by providing important insights on the interlinkages, but it cannot foretell the future. Decisions have to be made by governments and business without a full comprehension of all relevant interlinkages and possible consequences; tools are being designed that inform possible options. But, the CLEWS is a flexible modelling tool to quantitatively assess the food-energy-water NEXUS in the context climate change It serves as a good entry point to gather evidence to inform sustainable development policies, including implementation of 2030 Agenda and Paris Agreement NDCs It retains sector relevance while providing an integrated assessment It can contribute to the institutional coordination needed for integrated policy formulation 14 March 2017 4th UN Data Innovation Lab, University of Nairobi

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4th UN Data Innovation Lab, University of Nairobi

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