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This study aims to investigate the influencing factors and the processes for incorporating climate change mitigation actions into policies in the non-environment sector in Nepal. We use semi-structured interviews with policy actors such as national and sub-national policymakers, and respondents from the private sector and international development organizations active in Nepal. We also use thematic, narrative, and focused coding to analyze narrative data obtained from 12 respondents, and qualitative analysis of textual data from six non-environment sector policies to generate insights into the mainstreaming of climate change mitigation actions. A major finding from the study is that global environment-related initiatives like the Paris Agreement and the Sustainable Development Goals, and the green growth concept that aims to mitigate greenhouse gas (GHG) emissions, are influencing the policy discourse in Nepal. Consequently, climate change mitigation actions are integrated either as add-ons or as overriding policy objectives in non-environment sector policies. Our conceptualization of mainstreaming moves beyond the mere integration of policy objectives to focus on the collaborative practices of policy actors, the influencing factors, and the processes for incorporating climate change mitigation actions across non-environment sector policies.

While a growing body of studies has investigated the collaborative governance (CG) of dockless bike-sharing schemes (DBSS) worldwide, few offer close descriptions and analyses of stakeholder interactions in specific social contexts. Our study fills this gap by examining the development of CG of DBSS in Sydney, Australia between 2017 and 2020. The methodology is guided by an Integrative Framework for CG, drawing on qualitative analysis of policy documentation and semi-structured interviews with key DBSS participants from the public and private sector. Our findings reveal context-specific drivers and dynamics that shaped the development of particular forms of CG within Sydney’s DBSS.

The invisible and complex transfer of embodied carbon emissions makes the traditional production or consumption approach insufficient to inform emissions abatement actions because carbon communities have emerged during the transmission procedure of embodied carbon emissions. The carbon community—a group of sectors with more intensive embodied carbon emissions trades within the group than outside—provides the missing critical information about carbon abatement beyond the commonly used production and consumption approaches. This research aims to detect the carbon communities and examine the effect of community structure on sectors' direct carbon emissions. Unlike the industrial agglomeration in traditional economics and management studies, where the border is predefined in a geographical or administrative region, the hybrid input-output analysis and network analysis method detects the carbon communities data-driven, focusing on the embodied carbon emissions trades. Moreover, the hierarchical linear model examines the effect of community structure on sectors' direct carbon emissions to inform climate change policy-making and planning. The findings suggest around 19 carbon communities existing in China, which can advise local governments on their external cooperation strategies for a synergy. In addition, the regression results indicate that the increasing size and density of carbon communities can help mitigate sectors' direct carbon emissions.

The Scope 1, 2, and 3 emissions analysed in the OECM are defined and are presented for the 12 sectors analysed: (1) energy, (2) power and gas utilities, (3) transport, (4) steel industry, (5) cement industry, (6) farming, (7) agriculture and forestry, (8) chemical industry, (9) aluminium industry, (10) construction and buildings, (11) water utilities, and (12) textiles and leather industry. The interconnections between all energy-related CO2 emissions are summarized with a Sankey graph.

This study investigates, at the country level, the adverse effects of changes in metal inputs on the achievement of sustainable development goals (SDGs). It also highlights the relationships between metals use and various socio-economic consequences that urgently require decoupling in order to achieve the SDGs. We performed panel data analysis to evaluate, on a national scale and over a ten-year period (2004–2013), the impact of changes in the material flows of 11 metals on 96 SDG indicators corresponding to the 17 SDGs defined by the UN. On average, an increase in the material flow of the targeted metals was found to be correlated with a deterioration in approximately 10% of the 96 indicators. Among the affected SDGs, the adverse impact of metals on SDG 3 (Health), SDG 8 (Economic Growth), and SDG 16 (Peace, Justice, and Strong Institutions) was particularly noteworthy. More SDGs were negatively impacted in metal-mining countries than in metal-importing countries.

International audience; The increasing impacts of climate change, coupled with the Greta Thunberg effect, the findings of the Intergovernmental Panel on Climate Change (IPCC) reports, and varied environmental policy documents, are pointing to the need for urgent and cohesive climate action and mitigation frameworks. One potent solution, gaining global acceptance, is that of the Green New Deal (GND), positioned as a radical rethinking of political and economic structures in view of pushing sustainability at the forefront of national, regional, and global issues. With the model rapidly gaining ground in various geographies, and in different forms in view of contextualization needs, there is a need to better understand its evolution, knowledge structures, and trends. This paper thus sets forth to provide an understanding of the evolution and implementation of GND through a bibliometric analysis and science mapping techniques using VOSviewer and CiteSpace to identify the thematic focus of 1174 articles indexed in the Web of Science since 1995. To understand the thematic evolution of the field over time, we divided the study period into three sub-periods, namely 1995–2014, 2015–2019, and 2020–2021. These sub-periods were determined considering important milestones related to GNDs. Term co-occurrence analyses were then conducted to understand thematic focus and associated trends. Also, co-citation analysis and bibliographic coupling were other methods applied to identify major sources, authors, publications, and countries that have made more contributions to the development of research on GNDs. The findings of this paper can help both researchers and policy makers understand the evolution and trends of GNDs to better formulate GNDs strategies and policies in accordance with varying needs and geographies.

Water utility companies in urban areas face two major challenges: ensuring there is enough water for everyone during prolonged drought and maintaining adequate water pressure during the hours of peak demand. These issues can be overcome by applying data analytics and machine learning to the data gathered from digital water meters. For water conservation and demand management strategies to be effective, utility companies need to gain a better understanding of consumer behaviours, habits and routines. To accomplish this goal, we adapted a clustering approach to reveal residential water consumption patterns within metered data. In the experiment, we used two data sets (engineered features data set as well as the times of use and weighted probabilities of use data set) based on the data collected over 10 months from 306 households in Melbourne, Australia. For the engineered features data set, first, we identified the number of optimal clusters. We then performed extensive experiments to find the best clustering approach in terms of performance evaluation and clustering quality. We chose the hierarchical agglomerative clustering technique based on the nature of the data and the objective of the study. We observed that for the engineered features data set, k-means is the best performing clustering technique after considering performance metrics. For the other data set, we found that the number of clusters varies based on the type of water-consumption event, type of day (i.e., weekday or weekend), profiling interval and probability of use. In addition, we observed that insight into tap-water usage could be used to determine the population’s adaptation of hygiene practices in an unprecedented time, such as the COVID-19 pandemic. Finally, we recommend that future clustering studies also employ aligned socio-demographic data and other key features.

The inclusion of climate mitigation actions in the Nationally Determined Contributions and climate policies of low-income countries such as Nepal and Bangladesh means that policymakers are seeking to address both types of responses to climate change. This study assesses changes in policy paradigms pertinent to climate mitigation, in Nepal and Bangladesh for the period from 1992 to 2018. Policy paradigm refers to the framework of policymakers' ideas and strategies that influence the formulation of policies across different aspects. This research develops and uses an analytical framework which considers the following aspects of public policy: (i) problems and focus; (ii) content (policy instruments and financial resources); (iii) institutions and strategic interactions; and (iv) global environment-related initiatives. Relevant policies (18 for Bangladesh and 17 for Nepal) were analyzed and thematically coded using NVivo software. While most aspects showed notable change over time, the institutions and strategic interactions aspect showed only incremental change. Although primarily focussed on adaptation, a new policy paradigm that seems to have emerged post-2005 for Nepal and Bangladesh focuses on low carbon development, access to energy, sustainable transport, and sustainable agricultural practices. To operationalize the new policy paradigms in both countries, economic and market-based policy instruments that utilize the government's internal funding will need to support policies to minimize the impacts of changes in official development assistance.

Global energy transitions could fundamentally change flows of both minerals and energy resources over time. It is, therefore, increasingly important to holistically and dynamically capture the impacts of large-scale energy transitions on resource flows including hidden flows such as mine waste, as well as direct flows. Here we demonstrate a systematic model that can quantify resource flows of both minerals and energy resources under the energy transition by using stock-flow dynamics and the concept of Total Material Requirement (TMR). The proposed model was applied to the International Energy Agency’s scenarios up to 2050, targeting 15 electricity generation and 5 transport technologies. Results indicate that the global energy transition could increase TMR flows associated with mineral production by around 200–900% in the electricity sector and 350–700% in the transport sector respectively from 2015 to 2050, depending on the scenarios. Such a drastic increase in TMR flows is largely associated with an increased demand for copper, silver, nickel, lithium and cobalt, as well as steel. Our results highlight that the decarbonization of the electricity sector can reduce energy resource flows and support the hypothesis that the expansion of low-carbon technologies could reduce total resource flows expressed as TMR. In the transport sector, on the other hand, the dissemination of Electric Vehicles could cause a sharp increase in TMR flows associated with mineral production, which could offset a decrease in energy resource flows. Findings in this study emphasize that a sustainable transition would be unachievable without designing resource cycles with a nexus approach.

A number of empirical methods have been developed to study China's sectoral carbon emissions (CSCE). Measuring these emissions is important for climate change mitigation. While several articles have reviewed specific methods, few attempts conduct a systematic analysis of all the major research methods. In total 807 papers were published on CSCE research between 1997 and 2017. The primary source of literature for this analysis was taken from the Web of Science database. Based on a bibliometric analysis using knowledge mapping with the software CiteSpace, the review identified five common families of methods: 1) environmentally-extended input-output analysis (EE-IOA), 2) index decomposition analysis (IDA), 3) econometrics, 4) carbon emission control efficiency evaluation and 5) simulation. The research revealed the main trends in each family of methods and has visualized this research into ten research clusters. In addition, the paper provides a direct comparison of all methods. The research results can help scholars quickly identify and compare different methods for addressing specific research questions.

Advanced householder profiling using digital water metering data analytics has been acknowledged as a core strategy for promoting water conservation because of its ability to provide near real-time feedback to customers and instil long-term conservation behaviours. Customer profiling based on household water consumption data collected through digital water meters helps to identify the water consumption patterns and habits of customers. This study employed advanced customer profiling techniques adapted from the machine learning research domain to analyse high-resolution data collected from residential digital water meters. Data analytics techniques were applied on already disaggregated end-use water consumption data (e.g., shower and taps) for creating in-depth customer profiling at various intervals (e.g., 15, 30, and 60 min). The developed user profiling approach has some learning functionality as it can ascertain and accommodate changing behaviours of residential customers. The developed advanced user profiling technique was shown to be beneficial since it identified residential customer behaviours that were previously unseen. Furthermore, the technique can identify and address novel changes in behaviours, which is an important feature for promoting and sustaining long-term water conservation behaviours. The research has implications for researchers in data analytics and water demand management, and also for practitioners and government policy advisors seeking to conserve valuable potable-water resources.

Nepal and Bangladesh aim to reduce their greenhouse gas emissions as part of their commitments to implementing the Paris Climate Agreement and United Nations Sustainable Development Goals (SDGs). In addition, both countries are seeking to move from being categorized as low to middle-income countries. This study analyzes the empirical evidence on greening of economic growth in Nepal and Bangladesh between 1985 and 2016, and looks ahead to 2030 to discuss the future prospects in their efforts to deliver on both environmental and economic goals. To analyze their historical progress, six green growth indicators are used, and to look ahead to 2030, energy and material consumption models are used. For both countries, energy and material productivity improvements were mainly driven by structural changes in an economy, which is a transition from agricultural to service-based economies. Yet these are found to be insufficient to deliver green growth. An increase in the share of renewable energy in the energy mix and the absolute reduction of energy and material consumption in future are found to be important not only for greening the growth but also for delivering abovementioned commitments. Technological changes such as the substitution of biomass by electricity from renewable resources can be a part of sustainable strategy for reconciling the climate mitigation actions with graduation to the middle-income country category.

Detailed projections of the Former Soviet Union (FSU) fossil fuel production has been created. Russian production has been modelled at the region (oblast) level where possible. The projections were made using the Geologic Resource Supply-Demand Model (GeRS-DeMo). Low, Best Guess and High scenarios were created. FSU fossil fuels are projected to peak between 2027 and 2087 with the range due to spread of Ultimately Recoverable Resources (URR) values used. The Best Guess (BG) scenario anticipates FSU will peak in 2087 with production over 170 EJ per year. The FSU projections were combined with rest of the world projections (Mohr et al. 2015b), the emissions from the High scenario for the world are similar to the IPCC A1 AIM scenario.Supplementary informationThe online version contains supplementary material available at 10.1007/s40789-021-00449-x.

This chapter discusses the integration of mining with responsible consumption and production and rates it as a work in progress. It highlights the need to reduce adverse environmental and social impacts, not only from the mining and production of ores at individual sites, but also in transforming the systems and supply chains across the globe to sustainable patterns of consumption with increased material efficiency, looking towards a low-carbon circular economy. Current trends include the growing adoption of renewable energy in mining and minerals processing, better tailings and post-closure management, increased provenance and traceability through ‘responsible’ certification programs, design for circularity, through to a conceptualization of mining beyond processing terrestrial ores (or even from oceans and asteroids) to increased ‘urban mining’ of metals from scrap products and infrastructure and ultimately, business models providing metal-based services rather than commodity sales of ores and ingots. Partial progress against this sustainable development goal is being made, driven by investors, shareholders and societal pressure on mining companies and from supplier expectations of end-users of metals.

Digital or intelligent water meters are being rolled out globally as a crucial component in improving urban water management. This is because of their ability to frequently send water consumption information electronically and later utilise the information to generate insights or provide feedback to consumers. Recent advances in machine learning (ML) and data analytic (DA) technologies have provided the opportunity to more effectively utilise the vast amount of data generated by these meters. Several studies have been conducted to promote water conservation by analysing the data generated by digital meters and providing feedback to consumers and water utilities. The purpose of this review was to inform scholars and practitioners about the contributions and limitations of ML and DA techniques by critically analysing the relevant literature. We categorised studies into five main themes: (1) water demand forecasting; (2) socioeconomic analysis; (3) behaviour analysis; (4) water event categorisation; and (5) water-use feedback. The review identified significant research gaps in terms of the adoption of advanced ML and DA techniques, which could potentially lead to water savings and more efficient demand management. We concluded that further investigations are required into highly personalised feedback systems, such as recommender systems, to promote water-conscious behaviour. In addition, advanced data management solutions, effective user profiles, and the clustering of consumers based on their profiles require more attention to promote water-conscious behaviours.

Metals underpin essential functions in modern society, yet their production currently intensifies climate change. This paper develops global targets for metal flows, stocks, and use intensity in the global economy out to 2100. These targets are consistent with emissions pathways to achieve a 2 °C climate goal and cover six major metals (iron, aluminum, copper, zinc, lead, and nickel). Results indicate that despite advances in low-carbon metal production, a transformative system change to meet the society's needs with less metal is required to remain within a 2 °C pathway. Globally, demand for goods and services over the 21st century needs to be met with approximately 7 t/capita of metal stock-roughly half the current level in high-income countries. This systemic change will require a peak in global metal production by 2030 and deep decoupling of economic growth from both metal flows and stocks. Importantly, the identified science-based targets are theoretically achievable through such measures as efficient design, more intensive use, and longer product lifetime, but immediate action is crucial before middle- and low-income countries complete full-scale urbanization.

© 2018 Elsevier Ltd Advanced metering technologies coupled with informatics creates an opportunity to form digital multi-utility service providers. These providers will be able to concurrently collect a customers’ medium-high resolution water, electricity and gas demand data and provide user-friendly platforms to feed this information back to customers and supply/distribution utility organisations. Providers that can install low-cost integrative systems will reap the benefits of derived operational synergies and access to mass markets not bounded by historical city, state or country limits. This paper provides a vision of the required transformative process and features of an integrated multi-utility service provider covering the system architecture, opportunities and benefits, impediments and strategies, and business opportunities. The heart of the paper is focused on demonstrating data modelling processes and informatics opportunities for contemporaneously collected demand data, through illustrative examples and four informative water-energy nexus case studies. Finally, the paper provides an overview of the transformative R&D priorities to realise the vision.

Electrification of the transport sector will support its decarbonization, yet significantly change material requirements. This calls for an integrated modeling approach internalizing metal demand-supply dynamics in low-carbon scenarios to support the Paris agreement on climate change and sustainable material circulation. Here we develop a step toward the integrated simulation of energy-materials scenarios by unifying a stock-flow dynamics model for low-carbon scenarios using linear programming. The modeling framework incorporates lithium supply from both mines and end-of-life (EoL) recycling for projected use in electric vehicles on a global basis. The results show that supply constraints, which could become apparent from around 2030 in the case of current recycling rates (

Decoupling of resource consumption from economic growth is a key principle in the transition towards a circular economy. This study explores regional variation in the decoupling of waste generation from mean income in the Australian state of New South Wales (NSW), following the Waste Kuznet's curve (WKC) hypothesis. The WKC hypothesis tests for the existence of a relationship between waste and economic indicators conforming to an inverted-U shape that may indicate decoupling. A geographically and temporally weighted regression (GTWR) model is used to test the WKC hypothesis for municipal waste from 2011 to 2015. We identify municipalities conforming to the WKC hypothesis, and examine the socioeconomic and urban morphological characteristics of these municipalities. Results show that waste policy must be targeted to consider local variability in socioeconomics. Municipalities across rural NSW were found to conform to the WKC over the time frame. WKC-conforming municipalities had higher per-capita rates of waste generation, and lower mean incomes compared to non-conforming municipalities. Ratios of tipping point (global maximum) to mean income for WKC conforming municipalities were estimated between 0.8 and 2, indicating that these municipalities are in stages of relative, rather than absolute, decoupling. This study demonstrates the application of the WKC for examining decoupling, and highlights the importance of considering variations in regional characteristics when assessing the decoupling of waste generation from income. Findings also broadly suggest regionally specific policy making is required for circular economy transitions in NSW.

With China&rsquo, s commitment to peak its emissions by 2030, sectoral emissions are under the spotlight due to the rolling out of the national emission trading scheme (ETS). However, the current sector policies focus either on the production side or consumption while the majority of sectors along the transmission were overlooked. This research combines input&ndash, output modelling and network analysis to track the embodied carbon emissions among thirty sectors of thirty provinces in China. Based on the large-data resolution network, a two-step network reduction algorithm is used to extract the backbone of the network. In addition, network centrality metrics and community detection algorithms are used to assess each individual sector&rsquo, s roles, and to reveal the carbon communities where sectors have intensive emission links. The research results suggest that the sectors with high out-degree, in-degree or betweenness can act as leverage points for carbon emissions mitigation. In addition to the electricity sector, which is included in the national ETS, the study also found that the metallurgy and construction sectors should be prioritized for emissions reduction from national and local levels. However, the hotpots are different across provinces and thus provincial specific targeted policies should be formed. Moreover, there are nineteen carbon communities in China with different features, which provides direction for provincial governments&rsquo, external collaboration for synergistic effects.

The concept of resilience is a crucial part in crafting visions of desirable futures designed to withstand the widest variety of external shocks to the system. Backcasting scenarios are widely used to envision desirable futures with a discontinuous change from the present in mind. However, less effort has been devoted to developing theoretical frameworks and methods for building backcasting scenarios with a particular focus on resilience, although resilience has been explored in related sustainability fields. This paper proposes a method that helps design backcasting scenarios for resilient futures. A characteristic of the method is to delineate “collapse” futures, based upon which resilient futures are described to avoid the various collapsed states. In the process of designing backcasting scenarios, fault tree analysis (FTA) is used to support the generation of various risk factors and countermeasures to improve resilience. In order to test the effectiveness of the proposed method, we provide a case study to describe resilient energy systems for a Japanese community to 2030. Four expert workshops involving researchers from different disciplines were organized to generate diversified ideas on resilient energy systems. The results show that three scenarios of collapsed energy systems were described, in which policy options to be taken toward achieving resilient energy systems were derived.

The global transition to fundamentally decarbonized electricity and transport systems will alter the existing resource flows of both fossil fuels and metals; however, such a transition may have unintended consequences. Here we show that the decarbonization of both the electricity and transport sectors will curtail fossil fuel production while paradoxically increasing resource extraction associated with metal production by more than a factor of 7 by 2050 relative to 2015 levels. Importantly, approximately 32–40% of this increase in resource extraction is expected to occur in countries with weak, poor, and failing resource governance, indicating that the impending mining boom may result in severe environmental degradation and unequal economic benefits in local communities. A suite of circular economy strategies, including lifetime extension, servitization, and recycling, can mitigate such risks, but they may not fully offset the growth in resource extraction. Our findings underscore the importance of institutional instruments that enhance the resource governance of entire low-carbon technology supply chains, along with circular economy practices. In the absence of such actions, the decarbonization of electricity and transport sectors may pose an ethical conundrum in which global carbon emissions are reduced at the expense of an increase in socio-environmental risks at local mining sites.

Given recent circular economy policy and waste minimisation targets, there is a significant opportunity to fundamentally change the way waste is managed in Australia, and re-focus waste management to promote resource recovery and efficiency. Detailed data on household waste generation can assist decision makers in targeting waste minimisation incentives, improving resource recovery and circularity, identifying specific technology and infrastructure gaps and informing future development. Unfortunately, high-resolution spatial estimates of waste generation at the property lot scale is typically unavailable. This study presents a novel spatial model developed to estimate waste generation data at the property lot level. Utilising census data at multiple spatial scales and council waste generation data, we apply our model to estimate quantities of residual waste, dry recyclables and garden waste generated for more than 1,200,000 property lots in the Sydney metropolitan area, Australia. Results show the spatial distribution of estimated household waste generation, achieving a high degree of accuracy when compared to validation data. To illustrate the application of our results in the context of identifying ideal areas for waste processing facilities, we analyse the spatial distribution of available garden waste arising from property lots. An area of intense garden waste generation was identified, indicating a supply area of approximately 13km 2 in northern Sydney that can support a facility of approximately 20,000t throughput a year. Our analytical approach presented is novel, and has practical application for locating waste processing facilities; analysing efficient kerbside waste collection services; and in informing data driven urban waste management strategies.

© 2017 Informa UK Limited, trading as Taylor & Francis Group. This paper reports on the short- and long-term impacts of online water-use feedback provided via a smart metering trial involving 120 households in New South Wales, Australia. Near-real time water consumption feedback was provided via an online portal to half of the sample. Water consumption was uniquely analysed one year pre- and post-intervention, and in conjunction with login data. During one year of available access, the intervention group saved an overall average of 24.1 litres per household per day (L/hh/d) (4.2%). Regression analysis showed the significant savings of active users related specifically to portal login activity. Significant short-term effects persisted for 42 days, averaging at 63.1 L/hh/d. The article discusses the implications for research and practice, including a consideration of how, in addition to providing ongoing access, online portals could be leveraged further by water authorities to help meet urgent short-term supply constraints such as in drought.

© 2016 Elsevier Ltd Understanding energy–water system interactions is critical to the effective management of urban infrastructure. This paper explores the potential for hydropower as a co-benefit in a novel operating regime for Sydney׳s main water reservoir (Warragamba Dam). Hydropower could be generated as part of storage level management in the reservoir aimed at introducing flood retention ‘airspace’ (to mitigate downstream flood risk from extreme rainfall) whilst augmenting the use of installed desalination capacity to maintain secure supplies of water. A purpose-built systems dynamics model provides the mechanism for evaluating and comparing future operating scenarios over a 25 year period (i.e. until 2040). Importantly, the findings reveal the potential for desalination plants, integrated into a populous city׳s water supply network, to satisfy a much broader planning agenda. Specifically, the study provides evidence that Sydney's interdependent goals of deferring capital intensive flood storage works, maintaining water security, better utilising existing desalination and hydropower assets, and increasing renewable energy generation can be achieved through applying systems thinking to a complex citywide water planning problem. The work also makes a valuable contribution to the energy–water nexus literature at the under-explored city-scale.

© 2016 Elsevier Ltd Smart water metering (SWM) introduces new opportunities to engage householders about water use based on detailed information. Water utilities must decide how to embrace these opportunities, but remain hesitant due to limited available experience and knowledge, which risks delaying the benefits of involving householders more fully in SWM and more sustainable water consumption. An implementation framework is developed outlining the key strategic, practical and evaluative elements in decision-making for detailed feedback programs by drawing on the literature and first-hand experiences of two feedback trials involving SWM. Existing approaches are reviewed and recommendations are provided to advance more well-considered approaches and realise benefits regarding sustainable water use.

Shifting climate patterns are causing extreme drought and flooding across the globe. This combined with the world’s burgeoning population and insatiable thirst for water requires water service providers to think differently about the limited resources they manage. In Australia, the severe drought at the beginning of the century caused dams to fall to record levels. In response, many state governments invested heavily in rain-independent supplies such as desalination to augment and diversify traditional sources. However, extreme rainfall soon followed the drought, filled reservoirs and caused flooding in many locations leaving billions of dollars worth of damage and new water infrastructure standing idle. This is the case in Sydney, where the new desalination plant is still not used and the potential for major flooding has raised concerns over the safety of the large population downstream of the dam. This paper explores the growing need to understand the relationship between drought, flooding and infrastructure optimisation. The paper focuses on Sydney to illustrate the application of a system dynamics model. The new model explores options for raising the dam wall, offering airspace to assist flood protection, in contrast to options to lower the dam full supply level and utilise idle desalination capacity to fill the water security gap created. The illustrative results, using publicly available data, find that by lowering the dam water levels and operating desalination, significant flood protection can be achieved at a similar cost to raising the dam wall. The paper demonstrates the importance of optimising existing and new water resources for multiple purposes and how system dynamics modelling can assist water service providers in these complex investigations.