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A nexus approach for sustainable urban EnergyWater-Waste systems planning and operation Xiaonan Wang, Miao Guo, Rembrandt H.E.M. Koppelaar, Koen Haziel van Dam, Charalampos P Triantafyllidis, and Nilay Shah Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b04659 • Publication Date (Web): 31 Jan 2018 Downloaded from http://pubs.acs.org on January 31, 2018
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Environmental Science & Technology
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A nexus approach for sustainable urban Energy-Water-Waste
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systems planning and operation
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Xiaonan Wanga,b*, Miao Guoa*, Rembrandt H.E.M. Koppelaara,c, Koen H. van Dama,
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Charalampos P. Triantafyllidisa,d, and Nilay Shaha
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a
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London SW7 2AZ, UK
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b
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117585
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c
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d
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* Email:
[email protected] [email protected] 12
Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London,
Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore
Centre for Environmental Policy, Imperial College London, South Kensington, London SW7 2AZ, UK Department of Chemical Engineering, University College London, London WC1E 7JE, UK
Tel: +65 6601 6221 +44 020 7594 5705
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ABSTRACT:
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Energy, water and waste systems analyzed at a nexus level is key to move towards more
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sustainable cities. In this paper, the “resilience.io” platform is developed and applied to emphasize
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on waste-to-energy pathways, along with the water and energy sectors, aiming to develop waste
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treatment capacity and energy recovery with the lowest economic and environmental cost. Three
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categories of waste including wastewater (WW), municipal solid waste (MSW) and agriculture
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waste are tested as the feedstock for thermochemical treatment via incineration, gasification or
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pyrolysis for combined heat and power generation, or biological treatment such as anaerobic
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digestion (AD) and aerobic treatment. A case study is presented for Ghana in Sub-Saharan Africa,
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considering a combination of waste treatment technologies and infrastructure, depending on local
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characteristics for supply and demand. The results indicate that the biogas generated from waste
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treatment turns out to be a promising renewable energy source in the analyzed region, while more
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distributed energy resources can be integrated. A series of scenarios including the business-as-
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usual, base case, natural constrained, policy interventions and environmental and climate change
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impacts demonstrate how simulation with optimization models can provide new insights in the
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design of sustainable value chains, with particular emphasis on whole-system analysis and
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integration.
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1. Introduction
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With rapid urbanization, particularly in the developing world, cities are facing sustainability
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challenges on the nexus of energy, water and waste commensurate to financially feasible and
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reliable infrastructure systems planning.1,2 The projected 50% increase in global population in 21st
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century will lead to 11.2 billion people by the end of this century.3 This combined with the non-
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OECD economic growth is expected to bring a 33% increase in energy demand globally by 2050.4
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Energy is the dominant contributor to Greenhouse Gas (GHG) emissions and waste-to-energy
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pathways have been identified by the IEA as one of the promising solutions for a low-carbon
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pathway towards the 2°C warming scenario.5 Moreover, rising waste generation yields additional
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GHG contributions and other environmental impacts. For instance, one-third of food produced
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globally is lost or wasted every year, which is responsible for over 7% of the GHG emissions and
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the waste of 250 km3 water (8.5% annual withdrawn).6 The choice of waste-to-energy
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technologies, their deployment and logistics of which depend on local conditions including
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demands and availability of resources and associated networks, imply that a spatially explicit
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representation and computer-aided planning is imperative.7 To support investments and
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operational decisions for sustainable infrastructure systems development in cities, systems
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modeling approaches can be deployed taking into account energy-water-waste cross-sectoral
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integration, spatial-temporal resource flows and allocation, long-term socio-economic and
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environmental targets as well as technical constraints from a whole systems perspective.8–10 Our
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thorough literature review shows that previous research has been conducted in the energy supply-
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demand optimization and system analysis under a developing country context, e.g., energy system
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planning and forecasting, remote-area distributed renewable energy, bioenergy research
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optimization and waste-to-energy systems.11–13 Applications in support of the energy-water-food
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nexus planning has become a central focus in the current research agenda of developing systematic
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modeling tools.14–18 However, there is a significant gap on the decision-making tools bridging
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water-energy-waste sectors with supply-demand needs at both spatial and temporal scales, despite
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the fact that many tools have been developed and used effectively for individual domains.19–21
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In this paper we present the application of an integrated decision-making tool based on the
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resilience.io platform, which provides a series of modules to allow forecasting of socio-
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demographic scenarios, simulating spatio-temporal activities, and planning investment and
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operational strategies to meet the Sustainable Development Goals (SDGs).22 We developed the
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platform to study urban systems interactions through socio-economic forecasting, agent-based
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modeling, and resource-technology network optimization. The consensus among multiple sectors
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in decision making using these simulation and optimization tools has been effectively applied to
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the water-energy-food nexus in previous research to deliver both economic and environmental
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benefits.23,24 By introducing life cycle sustainability assessment (LCSA) and optimization into the
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developed resilience.io platform, system-wide economic, environmental and social sustainability
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perspectives have been incorporated and bridged with supply and demand across multiple-sectors.
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LCSA addresses the overall sustainability impacts associated with infrastructure, individual
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sectors and their interrelationship, or the entire urban development plan from a cradle-to-grave
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perspective.25 In combination with assessing policy support, such as a Feed-In Tariff (FIT) scheme
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(remunerating the sales of surplus electricity from technology operation back to the grid), our
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modeling tool incorporated green financial investment strategies to investigate the policy
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implications on the system performance.26 In addition to the commonly existing centralized
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systems, decentralized technologies have also been incorporated into our model, where the
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application of a systematic optimization tool is of critical importance for transparent urban service
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systems planning.27 A case study and scenario-based approaches demonstrate our model’s
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applicability in a developing country context, where possible cross-sector solutions for water,
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energy, waste and food for Ghana were scrutinized.28
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The capital city of Ghana and its neighboring administrative districts form the Greater Accra
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Metropolitan Area (GAMA). It is a rapidly growing metropolitan region, where great efforts have
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been placed to improve local community livelihood and city sustainable development particularly
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in the water sector. Household access to piped water grew by more than 80% from 2000 to 2010
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and potable water provisioning has been improved dramatically in recent years. The city has seen
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large expansions of potable water treatment at the Kpone site north of the city-region (several
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hundreds of thousands m3 per day), as well as the addition of a 60,000 m3 per day desalination
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plant.29,30 However, the percentage of total wastewater treatment (WWT) declined from around
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10% to nearly zero between 2000 and 2010, whilst the population of GAMA grew from 3 to 4
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million people. Nearly all untreated wastewater therefore is discharged to the environment. Minor
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improvements have been made by introducing a lagoon-based treatment plant with a capacity of
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6,400 m3 per day at the University of Ghana Legon and efforts are underway to rehabilitate the
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Jamestown treatment plant. Yet, these facilities are far from being adequate and the WWT sector
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in this region still faces significant challenges on system development and maintenance due to the
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financial constraints which conflict with the increasing environmental concerns.
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A promising solution would be to recover the resources contained in the wastewater. Anaerobic
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digestion (AD) has been widely acknowledged as an effective technology for the energetic
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valorization of various organic feedstock which is not only constrained to the wastewater, but also
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applicable to organic solid wastes such as agro-industrial residuals and municipal solid waste.31
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AD can transform waste treatment from an energy consumer to a net producer due to a 2-4 times
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higher energy recovery from the waste than the AD operational demands.32 The generated biogas,
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as one of the highlighted prioritized renewable fuels, has multiple applications, e.g. direct
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and compressed as transport fuel. In fact, biogas has been recognized as a rising renewable energy
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source in both rural and urban areas of many African countries.33 Such a combined production of
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heat, electricity and fertilizer provides a transitional scheme for local residents in Ghana. It can
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allow the mitigation of being heavily relied on burning traditional biomass and waste (e.g.
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firewood and charcoal) for household cooking and heating, which in turn has caused numerous
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environmental, health and safety concerns. Ghana has the potential to install a large number of
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biogas plants (54-865 MW installed capacity per year by estimation).34
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To incorporate these renewable technologies into the waste treatment and energy generation
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process, policy regulations that provide energy market support would be required to promote waste
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recovery and further penetration of renewable sources. A tariff scheme, which has been generally
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acknowledged as an effective deployment policy internationally and could potentially be adopted
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in Ghana, has been modeled in our case study to explore the potential role of this governmental
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intervention scheme. The applicability of a whole-system decision support tool to the broader
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developing country context is successfully illustrated by the presented regional application.
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The rest of the article is structured as follows. Section 2 first describes the overall life cycle
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approach, optimization formulation and associated models, followed by a detailed description of
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data input mechanisms. Section 3 mainly demonstrates how the overall methodology is applied to
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a Sub-Saharan African city-region to achieve cost-optimal and sustainable development plans
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especially in the water, energy and waste treatment sectors. A series of scenarios are analyzed
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including the “business-as-usual”, “base case”, “natural constrained”, “policy interventions” and
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“GHG reduction” related.
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2. Methods
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We developed the resilience.io platform initially for integrated resource planning, which mainly
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addresses water and energy, as well as their nexus.22 It is of paramount importance to connect the
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waste treatment especially waste-to-energy pathways to these utilities. In this work, only electricity
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is studied as the representative energy product, while heat and fuel are not included in the supply-
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demand forces, but only analyzed as additional output of the energy sector to the economy. The
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ultimate goal is to develop the treatment capacity and energy recovery with the lowest economic
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and environmental costs.
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2.1 General framework
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The overall modeling framework and data flows are illustrated in Figure 1, where three key
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components are soft-linked via data feedback loops including the resilience.io platform,
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optimization and LCSA. LCSA is an evaluation tool accounting for three sustainability pillars i.e.
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life cycle environmental assessment (LCA), life cycle costing (LCC) and life cycle social
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assessment (S-LCA).
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The resilience.io platform incorporates a series of modules to perform forecasting of socio-
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demographic scenarios, simulation of spatial-temporal supply/demand profiles and planning
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investment and operational strategies to meet the SDGs for multiple sectors including water,
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energy, food and waste. For the urban region under investigation, a comprehensive database with
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respect to the current resource availability, waste treatment capacity and distribution is built based
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on national and local statistics. Key references include household water use,35 total water use and
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treatment capacity,36 waste-water treatment plants,37 new infrastructure projects,38, and household
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infrastructure access.39 More exhaustive cleaned and referenced datasets for these aspects have 7 ACS Paragon Plus Environment
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been deposited on the openAfrica data portal.40 The spatially explicit waste generation and
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resource demands are projected by using agent-based models embedded in the resilience.io
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platform. The behaviors of diverse population groups are captured, which are categorized by their
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geographical district (original and real-time in the agent-based simulation), age, gender, work
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force, income rank and access to infrastructure. The overall sustainability footprints of
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technologies and the whole socio-technical system have been evaluated using LCSA inclusive of
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social (e.g., job creation, gender equality), economic (including capital costs, operating and
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maintenance costs) and environmental (e.g., climate change) aspects. The module that optimizes
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the resource technology network uses the current pre-allocated infrastructure (existing on-the-
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ground technologies), as per above and described in the deposited openAfrica data for
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initialization.40 This module incorporates novel technologies for waste resource recovery taking
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into account land-use (e.g., land types and available area) and fiscal budget (e.g., as a maximum
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cap of total costs or relaxed to obtain the optimal planning) constraints, with an objective to
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minimize the system-wide economic and environmental costs. The results provide development
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plans for alternative technologies, infrastructure as well as production and transportation schedule
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in order to meet the SDGs.
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2.2 Optimization model structure
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The interrelationship between water, waste and energy systems is addressed through material and
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energy balances. The main optimization problem is formulated as a mixed-integer linear
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programming (MILP) problem, with a multi-objective function which accounts for capital
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expenditures (CAPEX), operating expenditures (OPEX), and environmental impacts (mainly
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GHG emissions, monetized by carbon trading prices). The additional environmental costs
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associated with the nexus, such as the impact on air quality, human health and ecosystem quality,
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are obtained from the life cycle impact analysis as part of the results, instead of directly being
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included as a cost function term in the objective function. The objective function as shown in Eq.
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(1) combines weighing factors (Wf) and metrics (m) including costs for investment (VIJ), O&M
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(VPJ), resource transportation (VQ), pipe and grid expansions (VY) and resource import costs (VI)
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with details given in Eq. (2). All spatial districts are represented by i, technology by j, resource by
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r, metric by m and time scale by t (minor time periods, e.g., hours in a day) and tm (major time
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periods, e.g., years). The detailed explanation of the variables, parameters and models can be found
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in the Supplementary Information (SI).
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Figure 1. Overall methodology for analysis of urban waste and energy sectors using decision support
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platform through Life Cycle Sustainability Assessment.
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Multiple waste streams are incorporated in the model including WW, MSW especially its organic
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fraction (OFMSW), and lignocellulosic agricultural waste (e.g., accumulated at plantations for
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treatment and energy recovery). Their geological distribution lays the foundation of quantifying
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the energy potential to be recovered through several pathways as per the superstructure diagram
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shown in Figure 2. Compared with traditional landfills or incineration to treat solid waste, catalytic
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thermal treatment (e.g., pyrolysis or gasification) or biochemical treatment (e.g., anaerobic
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digestion and aerobic process) can be promising avenue to recover energy from OFMSW in the
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form of heat and electricity. Meanwhile wastewater is often treated through chemical and aerobic
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processes or through anaerobic digestion, yielding a source of biogas for electricity or heating.
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Specifically, agricultural waste serves as a valuable resource for energy recovery via anaerobic
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digestion pathway.
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Figure 2. A superstructure diagram of waste and energy resource flows through multiple technologies.
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Z = ∑ m Wf (m, tm) M (m, tm)
(1)
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Where Wf (m,tm) and M(m,tm) represent the weighting factors and value for all metrics m
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evaluated in the objective function during time period tm respectively.
VM (m, tm) = ∑ j ∑ i VIJ ( j , i, m) INV ( j , i, m) + ∑ j ∑ i ∑ t VPJ ( j , i, t , m) P ( j , i, t , tm) + ∑ i ,i ' ∑ r ∑ t VQ(r , t , m) dist (i, i ') Q(r , i, i ', t , tm) + ∑ i ,i ' ∑ r VY (r , m) dist (i, i ') Y (r , i, i ', tm) + ∑ i ∑ r ∑ t VI ( r , t , m) IM ( r , i, t , tm)
(2)
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The optimization model implements a set of constraints to drive the system in satisfying the
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respective waste treatment needs and electricity demand. First, equality constraints were
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introduced to achieve mass and energy balances at each level for every technology at all spatial
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zones and during each time period. Secondly, available operable capacities for each technology
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determine the upper limits for operations. Flow limits based on pipe, transportation network and
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grid connections are also imposed, which are defined by the associated geometric distance in
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kilometers (km). Finally, the investment allowance provides an upper bound of the total allowed
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cost for further infrastructure expansion. Besides the present steady-state system planning, future
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conditions are also predicted and planned through the forecasting models accounting for key
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parameters, such as the evolution of the population, income, and employment status. The
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optimization problem was solved using the open-source GNU Linear Programming Kit (GLPK),
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as the default solver freely available for the published platform, to allow an expanded access on
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the user-side.
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2.3 LCSA and data input
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The ‘cradle-to-grave’ LCSA approach generalized as Eq. (3) has been adopted to evaluate the
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sustainability footprints of waste treatment and recovery, including gasification, pyrolysis,
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incineration and anaerobic digestion. The mid-point characterization approaches were adopted in
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the LCSA evaluation, e.g. CML baseline41 for LCA; cost-sub categories (e.g. operational cost,
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capital cost, labor costs) for LCC analyses42. As summarized in Table 1, the capital inputs and
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operation are modeled within the defined LCA system boundary based on the functional unit ‘per
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day operation for treating WW and/or bio-solid waste (including OFMSW and agricultural waste)’.
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An economic allocation is applied to the processes with multiple products, thus zero impacts
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caused by the upstream waste-generating process have been assigned to WW and OFMSW (by-
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products). A stoichiometric carbon-counting approach was used to ‘track’ the carbon flows from
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embedded carbon in waste chemical components (including carbon ‘sequestration’ into the
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agricultural waste at crop growth stage) and through waste treatment and recovery, as well as the
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release of carbon during anaerobic digestion and combustion.
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The spatially-explicit agricultural land use maps of the studied region were derived from statistical
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reports and academic studies27,43–45 which combined the biomass partitioning ratio, leading to an
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estimated distribution of agricultural lingocellulosic waste. These include the above-ground
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lignocellulosic residual fractions of cassava, maize and banana plants. Existing waste treatment
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facilities and future demographic changes were also derived from multiple published data
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sources.46–50 The empirical data was used in the LCSA evaluation of organic waste treatment and
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recovery (i.e. gasification, pyrolysis, incineration) based on the operation and capital input-output
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flows generated from the WRATE (Waste Resources Assessment Toolkit for the Environment
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generated by Golder Associates) model and empirical and operational data derived from a UK 12 ACS Paragon Plus Environment
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commercial AD plant.51 Data for other existing or potential technologies for electrical power sector
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such as hydroelectric, natural gas, coal and renewables (mainly PV and wind) were obtained from
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a combination of national reports and local data collection (see details in our previous work and
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references 52,53). Further process descriptions and data flows are detailed in the SI.
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Economic costs are analyzed through empirical process models with respect to input-output data
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of materials, feedstock, utilities and other consumed resources. The economic and social costs are
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then obtained through life cycle costing based on engineering estimation methods for each
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technology according to its characteristics. Table 1 provides an overview of environmental and
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economic performance in the waste treatment, especially waste-to-energy applications as an
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example. More information about the general energy sector including hydroelectric, fossil fuels
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and renewable energy generation can be found in the SI. 𝑖𝑖𝑖𝑖 𝑜𝑜𝑜𝑜𝑜𝑜 𝑜𝑜𝑜𝑜𝑜𝑜 𝑖𝑖𝑖𝑖 𝐸𝐸𝐼𝐼𝑘𝑘𝑘𝑘𝑘𝑘 = � � 𝐸𝐸𝐸𝐸𝐸𝐸𝑟𝑟,𝑘𝑘𝑘𝑘𝑘𝑘 𝑋𝑋𝑟𝑟,𝑠𝑠 + � � 𝐸𝐸𝐸𝐸𝐸𝐸𝑐𝑐,𝑘𝑘𝑘𝑘𝑘𝑘 𝑋𝑋𝑐𝑐,𝑠𝑠 𝑟𝑟
240 241
𝑠𝑠
𝑐𝑐
(3)
𝑠𝑠
Where the variable 𝐸𝐸𝐼𝐼𝑘𝑘𝑘𝑘𝑘𝑘 denotes the total sustainability impacts of a given waste treatment and
recovery process (per functional unit) expressed as Key Performance Indicator kpi (e.g. cost and
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𝑖𝑖𝑖𝑖 GHG). 𝐸𝐸𝐼𝐼𝑘𝑘𝑘𝑘𝑘𝑘 is determined by the characterisation impact factors for input resource r (𝐸𝐸𝐸𝐸𝐸𝐸𝑟𝑟,𝑘𝑘𝑘𝑘𝑘𝑘 )
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s.
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The uncertainty analysis toolbox for LCSA is available under the integrated modeling framework,
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but exceeding the spectrum of the current scope. Under our uncertainty analysis tool, statistical
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methods (maximum likelihood estimation and goodness of fit) and expert judgement based
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approach (pedigree matrix) have been developed to quantify the inventory uncertainties due to
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cumulative effects of data variability or inventory uncertainties. At LCSA impact assessment level,
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𝑜𝑜𝑜𝑜𝑜𝑜 𝑖𝑖𝑖𝑖 𝑜𝑜𝑜𝑜𝑜𝑜 or emitted compound c (𝐸𝐸𝐸𝐸𝐸𝐸𝑐𝑐,𝑘𝑘𝑘𝑘𝑘𝑘 ) and the input or output flows (𝑋𝑋𝑟𝑟,𝑠𝑠 𝑜𝑜𝑜𝑜 𝑋𝑋𝑐𝑐,𝑠𝑠 ) at a life cycle stage
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a Monte Carlo simulation approach is introduced to estimate the uncertainties in characterised
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results introduced by the statistical variability or temporal, geographical and technology gaps in
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the inventory data. Such methodology functions can greatly amplify the confidence in the research
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findings and provide more robust and tangible evidence in the support of optimal decision-making.
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However, uncertainty analyses are not demonstrated in this paper but will be investigated in
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follow-up studies. Table 1 Model configuration for waste treatment and recovery technologies
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Treatment technology
Key operational parameter a
Waste streams (Input)
Life span a
Technology description a
Incineration
OFMSW
25year
Large-scale Efficiency=19% b moving grate incinerator
Gasification
OFMSW
Grate gasification
Efficiency= 18.8% b
Pyrolysis
OFMSW WW
Twin rotary kiln pyrolysis Continuous feeding thermophilic twostep digestion
Efficiency=13.3% b
Large-scale AD
25year 25year 20year
a
257 258 259 260 261 262 263 264 265
Efficiency= 17.2% b OLR for WW= 3.2 kg COD/m3/day c OLR for solid waste = 6 kg COD/m3/day
Biogas 94% COD conversion d OFMSW and Biogas -86% WW COD c conversion d OFMSW Corn Stover Literature-based biogas Cassava leaf estimation e Banana leaf Small-scale OFMSW 15Small-scale single Efficiency= 18.7% b Biogas: 86% AD year step digestion OLR=6 kg COD conversion d COD/m3/day c Banana leaf Literature-based Corn stover biogas Cassava leaf estimation e a. The representative AD and thermochemical pathways were modeled based on the operation and capital input-output flows generated from WRATE model and empirical and operational data derived from a UK commercial AD plant.54,55 b. Efficiency refers to the surplus electricity and heat generated and exported to grids from waste energy recovery. In incineration, on-site operation and transformation loss account for 22.1% electricity generated. Electrical conversion efficiency in AD is 20.35% with 15.7% and 8.1% of the generated electrical power consumed to operate large-scale and small-scale AD respectively.54,55 c. Organic loading rate (OLR) is measured in terms of the chemical oxygen demand (COD) of feed to a unit volume of digester per unit time56;. ORL of 3.17 kg COD/m3/day and 6 kg COD/m3/day was
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assumed for WWT and AD co-digestion (refer to co-digestion of wastewater and organic solid waste) , respectively.57,58 d. The COD removal efficiency for WWT and AD co-digestion was assumed as 99% (94% converted to biogas, 5% to cell mass) and 91% (86% converted to biogas, 5% to cell mass) respectively.54,55 e. The biogas production potential were estimated based on the empirical work on AD of lignocellulosic biomass as well as the representative data obtained from literature on chemical composition and characterisation of different waste streams in Ghana. 47,49,57–60
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3. Scenarios analysis and results
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The Greater Accra Metropolitan Area (GAMA) in Ghana, which has a large potential for energy
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recovery from waste, is studied here for its optimal development strategies. The model aims to
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satisfy waste treatment needs and electric power consumption for the region divided into sixteen
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functioning zones. We first forecast the required demand from base year 2010 until 2030 and
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benchmark it with statistical reports. A series of scenarios including “business-as-usual”, “base
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case”, “resource constrained”, “policy interventions” and “GHG reduction” are analyzed
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sequentially.
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3.1 Business-as-usual and base case scenarios
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A total of seven wastewater treatment methods, six municipal solid waste (including WW and
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MSW co-digestion) and two agro-waste treatment technologies for three locally common
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agricultural products (banana, cassava, and corn) were analyzed with respect to waste-to-energy
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pathways. An additional set of thirteen power generation technologies which are existing or
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promising to be utilized in Ghana are also accounted for in the case study to evaluate how energy
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recovered from waste fits within the entire power system.
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In a business-as-usual scenario, the existing infrastructure has little capacity to treat wastewater
289
and MSW and only 63% of total citizens’ electricity demand satisfied. Therefore, the resilience.io
290
model can largely contribute in providing an optimized investment and operations plan for all
291
potential and existing technologies which in return minimizes the total system cost. 15 ACS Paragon Plus Environment
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By investing on the available array of technologies, the municipal facilities can gradually meet all
293
citizens’ essential requirements and gradually reach each SDG by 2030. The CAPEX investment
294
to meet this goal was estimated around 16.87 billion USD for the GAMA region’s waste treatment
295
and power sector, excluding sunk investments in existing units evaluated in the base year (2010),
296
while the yearly OPEX by 2030 is estimated at 439.0 million with an associated 22.12 million
297
metric tonnes CO2 equivalent GHG emissions. It is evident from the results that large-scale
298
anaerobic digestion is a more effective solution among all wastewater treatment approaches, while
299
gasification and large-scale anaerobic co-digestion are suggested for MSW treatment. Combined
300
Cycle Gas Turbines (CCGT) and onshore wind energy conversion systems have dominant
301
advantages among power supply technologies due to their relatively low costs, accounting 63%
302
and 33% of total power supply respectively. However, the adoption of power facilities is limited
303
by the availability of fuel sources and renewable energy potential, especially for large-scale
304
application, which makes the “base case” optimized plan rarely feasible in real-world conditions.
305
To obtain a more realistic development plan, location and resource-specific constraints are added
306
when generating the investment and operation strategies, so that the environment variability is
307
appropriately accounted for.
308
3.2 Scenarios with natural resources constraints
309
In the following scenario of resource constrained planning, land and resource constraints are
310
imposed to suggest a localized technology selection. For example, the extensive land use of wind
311
energy conversion systems and water bodies for large hydro plants are not available in most
312
regions and agricultural conditions render significant regional differences. Accounting for these
313
realistic factors, the results indicate a bio-economy scenario in which agro-wastes are fully utilized
314
for energy recovery. Both large and farm-scale anaerobic digestion is preferable in all cases with 16 ACS Paragon Plus Environment
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benefits of energy recovery from waste. A combination of gasification and incineration (solid
316
waste), large-scale AD (WW and OFMSW co-digestion, agro-waste digestion), and farm-scale
317
AD (agro-waste) provides waste treatment strategies at the lowest cost. Figure 3 below gives an
318
overview of the most financially efficient technology mix with respect to dispatched capacities
319
that fulfil the total power demand. The minimized cost for a resource constraint scenario is higher
320
than the base case, with CAPEX requiring 34.91 billion USD over 20 years; annual OPEX was
321
found to be 516.9 million with all infrastructure in place, which is equivalent to 79.6 USD per
322
capita for the whole waste treatment and power sectors and coupled with 54.95 million metric
323
tonnes CO2 equivalence.
0.2993%
0.0013% 40.08%
45.42%
42.3731% 57.3263% 8.27% 2.08% 4.16% decentralized_activated_sludge_system
large_AD_co
large_AD_agro
decentralised_aerobic_treatment_plant
farm_AD_agro
incinerator_MSW
large_AD_co
gasification_MSW
large_AD_WW
(a) Model suggested wastewater treatment (b) Model suggested municipal solid waste and technologies (by wastewater handling agro-wastes treatment technologies (by solid capacity).
waste handling capacity).
324
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44% 3% 4% 13% 4% 1% 43%
1%
325
gas_CCGT coal_IGCC large_AD_co large_AD_WW large_AD_banana large_AD_casava large_AD_corn farm_AD_banana farm_AD_casava farm_AD_corn incinerator_MSW gasification_MSW solar_P-si-ground solarthermal_trough hydro_small wind_onshore hydro_large biofuel_cassava wind_offshore
326
(c) Optimal power supply technologies (by generation capacity).
327
Figure 3. Technology mix with minimal costs for meeting SDG goals in 2030 (scenario with
328
natural resources constraints).
329
The results suggested that the large-scale anaerobic digestion is the preferred solution among all
330
wastewater treatment routes, while gasification and large-scale anaerobic co-digestion are
331
suggested for MSW treatment. To have an in-depth understanding of the life-cycle performance
332
of the obtained technology landscape, Figure 4(a) further demonstrates the energy generation from
333
multiple waste-to-energy pathways, plus commonly exploited fossil fuel and renewable energy
334
generation plants for GAMA in 2030. It is obvious that fossil fuels still play a main role in energy
335
provision; this can be explained by the cost advantages of Coal Integrated Gasification Combined
336
Cycle (IGCC) and Gas CCGT over other power supply technologies despite their carbon-intensive
337
nature (Figure 4(a)). Additionally, as a key metric in the social LCA, labor hours required to
338
operate the waste treatment and power utility infrastructure imply the creation of 2,134 full-time
339
job per year (Figure 4(b)).
340
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Power Generation / kWh/year
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341
1.0E+10
1.0E+09
1.0E+08
1.0E+07
1.0E+06
(a) Power generation per year of selected power generation technologies.
342 AKWAPIM_SOUTH GA_SOUTH GA_EAST TEMA_METROPOLITAN VOLTA_RIVER KPONE_KATAMANSO LA_NKWANTANANG_MADINA LEDZOKUKU_KROWOR ADENTAN NSAWAM_ADOAGYIRI AWUTU_SENYA_EAST ACCRA_METROPOLITAN GA_CENTRAL ASHAIMAN GA_WEST
Thousands
LA_DADE_KOTOPON
343
0
100
200
300
400
500
344
(b) Labor needs (hours equivalent per year) in each studied region in GAMA from waste and
345
energy sectors.
346
Figure 4. Model suggested capacities and production rates per year for technologies in 2030
347
(scenario with natural resources constraints). 19 ACS Paragon Plus Environment
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348
3.3 Scenarios with policy interventions
349
The previous section illustrated how spatial constraints of natural resources can potentially affect
350
investment allocation. Additionally, decision makers also face political challenges or the need to
351
take into account other key considerations outside the model’s scope, for instance to implement
352
interventions which encourage, penalize or even ban certain technologies. The materialized
353
approach adds flexibility to the users in imposing user-based interventions and obtain an optimized
354
plan within the frame of these user-defined conditions.
355
As a commonly adopted policy intervention in the energy sector, a feed-in tariff is taken as an
356
example, which allows surplus power generation to be sold back to the grid. This provides
357
advantageous prices depending on different sources, so as to encourage higher penetration of
358
renewable sources in the overall electricity supply scheme. Local feed-in rates for wind, solar,
359
hydro and gas, recovered from wastes and biomass, were obtained from the Ghanaian domestic
360
Public Utilities Regulatory Commission (PURC). The results of a scenario with these tariffs are
361
shown in Figure 5(a) below and demonstrate how government enforced incentives can promote
362
clean energy technologies to boost the waste-to-energy utilization. With the same capital
363
investment, operational costs are significantly reduced mainly due to a selection of different
364
technologies benefiting from the feed-in-tariffs. Only 24.3 USD per capita per year of OPEX is
365
required to satisfy all waste treatment and energy demand, which is a 69.5% reduction when
366
compared with the OPEX of 79.6 USD in the previous case (no FIT set). The map gives an
367
overview of the studied region with the pie charts denoting the proportion of energy recovered
368
from local waste sources including WW, MSW and agro-waste in the total electricity supply from
369
the optimized results, for the SDG target year of 2030. The other suggested investment strategies
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370
and operation plans for each region are returned by the model from 2010 to 2030, where the SDGs
371
are expected to be gradually met.
(a)
Waste-to-Energy
district (with FIT).
penetration
by (b) Economic and environmental performance with different carbon price and enabled CCS technology.
372
Figure 5. Performance indicators of the whole waste and energy systems to satisfy total demand
373
in year 2030 (scenarios with policy interventions through FIT and carbon price).
374
3.4 Scenarios with environmental and climate change impacts
375
In this scenario, we specifically focus on the climate change impacts from a life cycle perspective.
376
When the carbon trading price increases (especially to multiples of the current level of 12.5 USD
377
per metric tonnes carbon) the overall dominant weighting of GHG emissions becomes more
378
significant in the total cost. This leads the energy sector towards a low-carbon transition. Although
379
it comprises a certain amount of economic benefits, long-term sustainability is witnessed
380
especially when coupled with novel technology selection. For example, advanced carbon capture
381
and storage (CCS) technologies can be introduced as an option to partly substitute traditional fossil
382
fuel plants for cleaner energy supply, instead of fully switching to renewable resources. The cost 21 ACS Paragon Plus Environment
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383
therefore does not obtain a significant increment (less than 18%), while the CO2 equivalent GHG
384
emissions can be reduced by more than 70.9% when compared with the scenarios without CCS
385
facilities. A total of 1,749 jobs is required to operate the infrastructure in all the studied sectors.
386
Figure 5(b) further compares each cost category under the scenarios of low, medium and high
387
carbon prices, indicating that the costs of decarbonisation are modest, with additional job creation
388
being beneficial for the whole society. From a local government’s perspective currently the costs
389
cannot be fully met domestically. This is due to the fact that the annual government budget for the
390
entire country fluctuates around 7 billion USD. The solutions could be rendered feasible if carbon
391
trading pricing is introduced at a level that provides sufficient returns for investors. This would
392
enable international capital investment to become more attractive in low carbon infrastructure
393
projects supporting waste-water-energy-land nexus.
394
4. Discussions
395
The development of waste-to-energy pathways brings substantial benefits to reduce GHG
396
emissions with improved resource allocation efficiency. Through a comprehensive LCSA, the
397
materials, energy, economic, environment and social impacts of a system can be evaluated
398
successfully. As a summary, we have presented a holistic approach to provide decision support for
399
urban waste treatment and power supply. The demand data is obtained by statistical sources and
400
agent-based simulation by formulating a system in which people, infrastructure and technologies
401
interact in a reciprocal relationship with the surrounding environment. The resource-technology-
402
network optimization model further evaluates available resources and returns the optimal
403
development/investment strategies under the given set of constraints, as explored in the respective
404
scenarios.
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405
The distinct optimal strategies emerged by varying policy (e.g., FIT) and technology options (e.g.,
406
land constraints, CCS), with substantial implications for overall technology systems and the extent
407
of waste-to-energy integration, as well as CAPEX and OPEX levels to SDG targets on water,
408
wastewaters and energy. Land constraints resulted in a reduction of gas CCGT and shift from
409
onshore wind to coal IGCC capacity and increased large-scale AD of crop residues and MSW, at
410
double the CAPEX and 17% more OPEX. By imposing a FIT to support waste utilization for
411
energy, previously marginal sources can be efficiently converted, providing a growth of share in
412
electricity generation, with a 70% reduction in OPEX. When the advanced CCS and a high carbon
413
price (125 USD/ton CO2) areintroduced, GHG emissions reductions of 70% can be achieved at a
414
manageable increase level of CAPEX (18%). The interaction between policies and technology that
415
emerges in the wastewater and energy sectors shows that distributed energy generation
416
technologies and waste-circular systems can be integrated. Water flows and wastewater treatment
417
sector have been formulated in the optimization model, contributing to the objective functions.
418
The optimization model presented in this study can be further expanded to address other water-
419
related issues such as the consumption of different categories of water and aquatic system quality
420
degradation (e.g. eutrophication, freshwater toxicity). This may lead to significant shift in the
421
optimal solutions e.g. coal-driven technologies, which may appear to be less competitive if
422
considering their water quality degradation profiles. Being able to address such cross-domain
423
interactions and focusing on the whole system or nexus level is one of the main advantages of the
424
presented approach herein.
425
As the Government of Ghana is currently elaborating on a long-term national development plan to
426
steer the country through the next 40 years, the platform shows clear evidence of benefits for
427
decision makers through our local dissemination events. Various government interventions can be 23 ACS Paragon Plus Environment
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428
studied in the model, such as carbon trading prices in collaboration with international agencies,
429
clean energy technology incentives by local governments, and most importantly satisfying demand
430
targets to achieve the sustainable development goals. Bio-renewable energy recovered from waste
431
treatment using anaerobic digestion was shown to be one of the most promising sustainable energy
432
source options in the studied region. The process serves as a reliable method to treat waste while
433
the digested sludge can be used as soil fertilizer or landfill cover. To implement such technologies,
434
it is necessary to tailor the policy measures and technology integration with local and international
435
support mechanisms. Capital expenditures for infrastructure in the GAMA context are borne
436
partially by dedicated national governmental funds for regions and via international loans. Novel
437
instruments for developing countries, such as the Green Climate Fund, can support the incremental
438
capital cost that emerged as part of a loan package, to ensure that novel infrastructure is climate
439
proof. Equally important is the demonstrated reduction in OPEX as this needs to be sustainable
440
locally from energy and water utility revenues, with the respective increase in job creation to
441
support such payments.
442
This study has set up an example for systematic resource nexus analysis for developing
443
countries/regions to leverage global development. Future work will explore a broader range of
444
waste-to-energy technologies and apply the presented methodology to other city regions. The
445
detailed investigation of various CCS and clean energy technologies and their broader
446
environmental impacts associated with costs of disposal and environmental remediation are out of
447
the current research scope and is part of our future work plans. We will also analyze additional
448
scenarios, such as different tariff and incentive settings applied to electricity derived from different
449
feedstocks across multiple regions, resilient adaptions to changes of natural environment,
450
international investment, regulations and roadmap for clean energy transitions. 24 ACS Paragon Plus Environment
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451
Acknowledgements
452
The authors are grateful to the Department for International Development (UK) for financial
453
support to the Future Proofing African Cities for Sustainable Growth project (grant number
454
203830). The Ghana Technical Group is greatly acknowledged for the useful discussions on data
455
input and case studies. We also deeply thank the Ecological Sequestration Trust (TEST) for
456
platform delivery at http://resilience.io to disseminate case study results and findings. XW thanks
457
MOE AcRF Tier 1 Grant for financial support to the project “Pathways to resilient Food-Energy-
458
Water-Waste Nexus” (P13-ChBE). MG would like to acknowledge EPSRC for financial support
459
through project ‘ReSBio’ (EP/N034740/1).
460 461
Supporting Information Available including:
462
Part A: Input parameters, Part B: Model details and Part C: Parameters and results of each
463
scenario. This information is available free of charge via the Internet at http://pubs.acs.org
464 465 466 467
Reference
468
(1)
469 470 471
Cohen, B. Urbanization in developing countries: Current trends, future projections, and key challenges for sustainability. Technol. Soc. 2006, 28 (1–2), 63–80.
(2)
Nilsson, M.; Griggs, D.; Visback, M. Map the interactions between Sustainable Development Goals. Nature 2016, 534 (15), 320–322. 25 ACS Paragon Plus Environment
Environmental Science & Technology
472
(3)
473
Page 26 of 33
United Nations General Assembly. Transforming our world: The 2030 agenda for sustainable development; 2015.
474
(4)
IEA. Key World Energy Statistics 2016; 2016.
475
(5)
International Energy Agency (IEA). World Energy Outlook 2016; Paris, 2016.
476
(6)
FAO. Food wastage: Key facts and figures; 2016.
477
(7)
Alhamwi, A.; Medjroubi, W.; Vogt, T.; Agert, C. GIS-based urban energy systems models
478
and tools: Introducing a model for the optimisation of flexibilisation technologies in urban
479
areas. Appl. Energy 2017, 191, 1–9.
480
(8)
Ramaswami, A.; Russell, A. G.; Culligan, P. J.; Sharma, K. R.; Kumar, E. Meta-principles
481
for developing smart, sustainable, and healthy cities. Science (80-. ). 2016, 352 (6288), 940–
482
943.
483
(9)
Leung Pah Hang, M. Y.; Martinez-Hernandez, E.; Leach, M.; Yang, A. Insight-Based
484
Approach for the Design of Integrated Local Food-Energy-Water Systems. Environ. Sci.
485
Technol. 2017, 51 (15), 8643–8653.
486
(10) IEA. Energy Technology Perspectives 2016; 2016.
487
(11) Othman, M. N.; Lim, J. S.; Theo, W. L.; Hashim, H.; Ho, W. S. Optimisation and targeting
488
of supply-demand of biogas system through gas system cascade analysis (GASCA)
489
framework. J. Clean. Prod. 2017, 146, 101–115.
490
(12) Delval, F.; Guo, M.; van Dam, K. H.; Stray, J.; Haigh, K.; Görgens, J.; Shah, N. Integrated
491
multi-level bioenergy supply chain modelling applied to sugarcane biorefineries in South
26 ACS Paragon Plus Environment
Page 27 of 33
492
Environmental Science & Technology
Africa. Comput. Aided Chem. Eng. 2016, 38, 2037–2042.
493
(13) Nguyen, R.; Guo, M.; Musikavong, C.; Bamroongrugsa, N.; Shah, N. Supply Chain
494
Optimisation of Nipa-based bioethanol industry in Thailand. Comput. Aided Chem. Eng.
495
2016, 38, 913–918.
496
(14) Martinez-Hernandez, E.; Leach, M.; Yang, A. Understanding water-energy-food and
497
ecosystem interactions using the nexus simulation tool NexSym. Appl. Energy 2017, 206,
498
1009–1021.
499
(15) Villarroel Walker, R.; Beck, M. B.; Hall, J. W.; Dawson, R. J.; Heidrich, O. Identifying key
500
technology and policy strategies for sustainable cities: A case study of London. Environ.
501
Dev. 2017, 21, 1–18.
502 503
(16) Endo, A.; Tsurita, I.; Burnett, K.; Orencio, P. M. A review of the current state of research on the water, energy, and food nexus. J. Hydrol. Reg. Stud. 2017, 11, 20–30.
504
(17) Bazilian, M.; Rogner, H.; Howells, M.; Hermann, S.; Arent, D.; Gielen, D.; Steduto, P.;
505
Mueller, A.; Komor, P.; Tol, R.; et al. Considering the energy, water and food nexus:
506
Towards an integrated modelling approach. Energy Policy 2011, 39 (12), 7896–7906.
507
(18) Zhang, J.; Campana, P. E.; Yao, T.; Zhang, Y.; Lundblad, A.; Melton, F.; Yan, J. The water-
508
food-energy nexus optimization approach to combat agricultural drought: a case study in
509
the United States. Appl. Energy 2017.
510
(19) Coutinho-Rodrigues, J.; Simão, A.; Antunes, C. H. A GIS-based multicriteria spatial
511
decision support system for planning urban infrastructures. Decis. Support Syst. 2011, 51
512
(3), 720–726. 27 ACS Paragon Plus Environment
Environmental Science & Technology
513 514
Page 28 of 33
(20) Chang, K.-H. A decision support system for planning and coordination of hybrid renewable energy systems. Decis. Support Syst. 2014, 64, 4–13.
515
(21) Lee, M.; Keller, A. A.; Chiang, P.-C.; Den, W.; Wang, H.; Hou, C.-H.; Wu, J.; Wang, X.;
516
Yan, J. Water-energy nexus for urban water systems: A comparative review on energy
517
intensity and environmental impacts in relation to global water risks. Appl. Energy 2017,
518
205, 589–601.
519
(22) Triantafyllidis, C. P.; Koppelaar, R. H. E. M.; Wang, X.; van Dam, K. H.; Shah, N. An
520
integrated optimisation platform for sustainable resource and infrastructure planning.
521
Environ. Model. Softw. 2018, 101, 146–168.
522
(23) Wang, X.; van Dam, K. H.; Triantafyllidis, C.; Koppelaar, R. H. E. M.; Shah, N. Water and
523
Energy Systems in Sustainable City Development: A Case of Sub-saharan Africa. Procedia
524
Eng. 2017, 198, 948–957.
525
(24) Bieber, N.; Ker, J. H.; Wang, X.; Triantafyllidis, C.; van Dam, K. H.; Koppelaar, R. H. E.
526
M.; Shah, N. Sustainable planning of the energy-water-food nexus using decision making
527
tools. Energy Policy 2018, 113, 584–607.
528 529 530 531
(25) Gong, J.; Darling Bc, S. B.; You, F. Perovskite photovoltaics: life-cycle assessment of energy and environmental impacts. Energy Environ. Sci. 2015, 8 (8), 1953–1968. (26) Couture, T.; Gagnon, Y. An analysis of feed-in tariff remuneration models: Implications for renewable energy investment. Energy Policy 2010, 38 (2), 955–965.
532
(27) Mohammed, Y. S.; Mokhtar, A. S.; Bashir, N.; Saidur, R. An overview of agricultural
533
biomass for decentralized rural energy in Ghana. Renewable and Sustainable Energy 28 ACS Paragon Plus Environment
Page 29 of 33
534 535 536
Environmental Science & Technology
Reviews. 2013, pp 15–22. (28) Case Studies - resilience.io - network of integrated tools and collaboration for financing and decision making http://resilience.io/stories/ (accessed May 12, 2017).
537
(29) Ghana. Statistical Service. 2000 Population and Housing Census: Greater Accra Region
538
Analysis of District Data and Implications for Planning; Ghana Statistical Service,: Accra,
539
2005.
540
(30) Nii Ardey Codjoe Tawiah John K Anarfi Clara Korkor Fayorsey, S. E.; Tawiah, E.; Ami
541
Amedoe Chapter Nine, J.; Gaisie, S. K.; Gyamfi Chapter Ten, S.; Anarfi, J. K.; Kpakpah
542
Chapter Eleven, M.; Botchie, G.; Owusu Kagya, J.; Ankomah, A.; et al. 2010 Population &
543
Housing Census: Regional Analytical Report Greater Accra Region. 2013.
544
(31) Sanscartier, D.; MacLean, H. L.; Saville, B. Electricity production from anaerobic digestion
545
of household organic waste in Ontario: Techno-economic and GHG emission analyses.
546
Environ. Sci. Technol. 2012, 46 (2), 1233–1242.
547 548 549 550 551 552 553 554
(32) Amigun, B.; Parawira, W.; Musango, J. K. Anaerobic biogas generation for rural area energy provision in Africa. Biogas 2012, 35–62. (33) Arthur, R.; Baidoo, M.; Antwi, E. Biogas as a potential renewable energy source: A Ghanaian case study. Renew. Energy 2011, 36 (5), 1510–1516. (34) GIZ. Biogas in Ghana: Sub-Sector Analysis of Potential and Framework Conditions. 2014, pp 1–30. (35) Lamptey, F. Determination of domestic water consumption pattern in Accra, Kwame Nkrumah University of Science and Technology, 2010. 29 ACS Paragon Plus Environment
Environmental Science & Technology
Page 30 of 33
555
(36) Van Rooijen, D. J. Implications of Urban Development for Water Demand , Wastewater
556
Generation and Reuse in Water-Stressed Cities: Case studies from South Asia and sub-
557
Saharan Africa, Lougborough University, 2011.
558
(37) TTZ Bremerhaven. Biotechnology for Africa’s Sustainable water supply: Evaluation of the
559
existing water biotechnologies and water management strategies in the targeted countries;
560
Bremerhaven, 2012.
561 562 563 564 565 566
(38) Vanderpuije, A. O. Sustainable Environmental Management and Improved Sanitation, a Shared Responsibility; 2016. (39) Bentsi-Enchill, N.; Cudjoe, S. N.; Sepah, E.; Anarfi, J. K.; Gaisie, S. K. 2010 Population & Housing Census: Regional Analytical Report Greater Accra Region; 2013. (40) Koppelaar, R. H. E. M. Greater Accra Population Socio Economics and Water Access. openAfrica. 2016.
567
(41) Guinée, J. B. Handbook of Life Cycle Assessment; Springer, 2002.
568
(42) Peters, M.; Timmerhaus, K.; West, R.; Timmerhaus, K. Plant design and economics for
569
chemical engineers; McGraw-Hill Education, 2003.
570
(43) Duku, M. H.; Gu, S.; Hagan, E. Ben. A comprehensive review of biomass resources and
571
biofuels potential in Ghana. Renewable and Sustainable Energy Reviews. 2011, pp 404–
572
415.
573 574
(44) Cauthen, J.; Jones, D.; Gugerty, M. K.; Anderson, C. L. Banana and Plantain Value Chain: West Africa EPAR Brief No. 239. 2013, No. 239, 1–25.
30 ACS Paragon Plus Environment
Page 31 of 33
575 576
Environmental Science & Technology
(45) Armah, M. Investment opportunity in Ghana: Maize, Soya and Rice Production and Processing. Millenn. Dev. Authority. Accra, Ghana 2010, 23.
577
(46) Ghana Statistical Service. Population and housing census summary report; 2012.
578
(47) Miezah, K.; Obiri-Danso, K.; Kádár, Z.; Fei-Baffoe, B.; Mensah, M. Y. Municipal solid
579
waste characterization and quantification as a measure towards effective waste management
580
in Ghana. Waste Manag. 2015, 46, 15–27.
581 582 583 584
(48) Anaglate, S. A.; Rahmaputro, S.; Ruiz, C.; Rojas-Solórzano, L. R. Comparison between Landfill Gas and Waste Incineration for Power Generation in Accra, Ghana. (49) Owusu Boadi, K.; Kuitunen, M. Municipal Solid Waste Management in the Accra Metropolitan Area, Ghana. Environmentalist 2003, 23, 211–218.
585
(50) Municipal solid waste incineration plant for Accra Brewery Limited (Ghana). 2016.
586
(51) Guo, M.; Stuckey, D. C.; Murphy, R. J. Is it possible to develop biopolymer production
587
systems independent of fossil fuels? Case study in energy profiling of polyhydroxybutyrate-
588
valerate (PHBV). Green Chem. 2013, 15 (3), 706.
589
(52) Kemausuor, F.; Obeng, G. Y.; Brew-Hammond, A.; Duker, A. A review of trends, policies
590
and plans for increasing energy access in Ghana. Renewable and Sustainable Energy
591
Reviews. 2011, pp 5143–5154.
592
(53) Energy Commission of Ghana. National Energy Statistics 2000-2013. 2014, No. April, 31.
593
(54) Guo, M.; Trzcinski, A. P.; Stuckey, D. C.; Murphy, R. J. Anaerobic digestion of starch–
594
polyvinyl alcohol biopolymer packaging: Biodegradability and environmental impact
31 ACS Paragon Plus Environment
Environmental Science & Technology
595
Page 32 of 33
assessment. Bioresour. Technol. 2011, 102 (24), 11137–11146.
596
(55) Guo, M.; Stuckey, D. C.; Murphy, R. J.; Galbe, M.; Zacchi, G.; Crocker, D.; Mitchell, B.;
597
Mohammad, J.; Cantrell, K.; Pittman, C. U. Is it possible to develop biopolymer production
598
systems independent of fossil fuels? Case study in energy profiling of polyhydroxybutyrate-
599
valerate (PHBV). Green Chem. 2013, 15 (3), 706.
600 601 602 603
(56) Monson, K. D., S. R. Esteves, A. J. Guwy, R. M. D. Anaerobic Digestion of Biodegradable Municipal Solid Wastes A Review. Univerisity of Glamorgan. (57) Awuah, E.; Abrokwa, K. A. Performance evaluation of the UASB sewage treatment plant at James Town (Mudor), Accra. 2008.
604
(58) Guo, M. Life Cycle Assessment (LCA) of Light-Weight Eco-composites Materials, 2012.
605
(59) Pang, Y. Z.; Liu, Y. P.; Li, X. J.; Wang, K. S.; Yuan, H. R. Improving Biodegradability and
606
Biogas Production of Corn Stover through Sodium Hydroxide Solid State Pretreatment.
607
Energy & Fuels 2008, 22 (4), 2761–2766.
608
(60) Chanakya, H. N.; Malayil, S. Techno-economic Potential for Value Added Products from
609
Digestion of Urban Solid Wastes and Rural Residues. Dyn. Biochem. Process Biotechnol.
610
Mol. Biol. 2013, 6 (special Issue 2), 38–42.
611
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