The Impact of Uncertainty in Climate Targets and CO2 Storage Availability on Long-Term Emissions Abatement

A major characteristic of our global interactive climate-energy system is the large uncertainty that exists with respect to both future environmental requirements and the means available for fulfilling these. Potentially, a key technology for leading the transition from the current fossil fuel-dominated energy system to a more sustainable one is carbon dioxide capture and storage. Uncertainties exist, however, concerning the large-scale implementability of this technology, such as related to the regional availability of storage sites for the captured CO₂. We analyze these uncertainties from an integrated assessment perspective by using the bottom-up model TIAM-ECN and by studying a set of scenarios that cover a range of different climate targets and technology futures. Our study consists of two main approaches: (1) a sensitivity analysis through the investigation of a number of scenarios under perfect foresight decision making and (2) a stochastic programming exercise that allows for simultaneously considering a set of potential future states-of-the-world. We find that, if a stringent climate (forcing) target is a possibility, it dominates the solution: if deep CO₂ emission reductions are not started as soon as possible, the target may become unreachable. Attaining a stringent climate target comes in any case at a disproportionally high price, which indicates that adaptation measures or climate damages might be preferable to the high mitigation costs such a target implies.     

The Effect of Green Investments in an Agent-Based Climate-Economic Model

Climate-economic modeling often relies on macroeconomic integrated assessment models (IAMs) that in general try to capture how the combined system reacts to different policies. Irrespective of the specific modeling approach, IAMs suffer from two notable problems. First, although policies and emissions are dependent on individual or institutional behavior, the models are not able to account for the heterogeneity and adaptive behavior of relevant actors. Second, the models unanimously consider mitigation actions as costs instead of investments: an arguable definition, given that all other expenditures are classified as investments. Both are challenging if the long-term development of climate change and the economy shall be analyzed. This paper therefore proposes a dynamic agent-based model, based on the battle of perspectives approach (Janssen [1]; Janssen and de Vries [2]; Geisendorf [3, 4]) that details the consequences of various behavioral assumptions. Furthermore, expenditures for climate protection, e.g., the transition of the energy system to renewables, are regarded as investments in future technologies with promising growth rates and the potential to incite further growth in adjoining sectors (Jaeger et al. [5]). The paper analyzes how a different understanding of climate protection expenditures changes the system’s dynamic and, thus, the basis for climate policy decisions. The paper also demonstrates how erroneous perceptions impact on economic and climate development, underlining the importance to acknowledge heterogeneous beliefs and behavior for the success of climate policy.     

How to Take into Account Uncertainty in Species Extinction Probabilities for Phylogenetic Conservation Prioritization

In this article, we are concerned with the general problem of choosing from a set of taxa T a subset S to protect in order to try to contribute to halting biodiversity loss as efficiently as possible given limited resources. The protection of a taxon decreases its extinction probability, and the impact of protecting the taxa of S is measured by the resulting expected phylogenetic diversity (ePD) of the set T. The primary challenge posed by this approach lies in determining the extinction probability of a taxon (protected or unprotected). To deal with this difficulty, the uncertainty about the extinction probabilities can be described through a set of possible scenarios, each corresponding to different extinction probability values for each taxon. We show how to determine an “optimal robust set” of taxa to protect, defined as the set of taxa that minimizes the maximum “regret,” i.e., the maximum relative gap, over all the scenarios, between (1) the ePD of T obtained by protecting the taxa of this set and (2) the ePD of T which would be produced by protecting the subset of taxa optimal for the considered scenario. In our experimental conditions covering 100 cases, this gap is almost always less than 1%. Consequently, the ePD of T obtained by protecting the taxa of the optimal robust set is not far from the maximum ePD of T that could have been obtained if we had known the true scenario. In other words, a way of escaping (in large part, at least) from the uncertainty related to the extinction probabilities of the taxa consists of choosing to protect those belonging to the optimal robust set. We also compare the optimal robust set to other relevant subsets of T.     

Mitigation Portfolio and Policy Instruments When Hedging Against Climate Policy and Technology Uncertainty

In this paper, we use a stochastic integrated assessment model to evaluate the effects of uncertainty about future carbon taxes and the costs of low-carbon power technologies. We assess the implications of such ambiguity on the mitigation portfolio under a variety of assumptions and evaluate the role of emission performance standards and renewable portfolios in accompanying a market-based climate policy. Results suggest that climate policy and technology uncertainties are important with varying effects on all abatement options. The effect varies with the technology, the type of uncertainty, and the level of risk. We show that carbon price uncertainty does not substantially change the level of abatement, but it does have an influence on the mitigation portfolio, reducing in particular energy R&D investments in advanced technologies. When investment costs are uncertain, investments are discouraged, especially during the early stages, but the effect is mitigated for the technologies with technological learning prospects. Overall, these insights support some level of regulation to encourage investments in coal equipped with carbon capture and storage and clean energy R&D.     

Approaches for performing uncertainty analysis in large-scale energy/economic policy models

A number of key policy insights have emerged from the application of large-scale economic/energy models, such as integrated assessment models for climate change. These insights have been particularly powerful in those instances when they are shared by all or most of the existing models. On the other hand, some results and policy recommendations obtained from integrated assessment models vary widely from model to model. This can limit their usability for policy analysis. The differences between model results are mostly due to different underlying assumptions about exogenous processes, about endogenous processes and the dynamics among them, differences in value judgments, and different approaches for simplifying model structure for computational purposes. Uncertainty analyses should be performed for the dual purpose of clarifying the uncertainties inherent in model results and improving decision making under uncertainty. This paper develops a unifying framework for comparing the different types of uncertainty analyses through their objective functions, categorizes types of uncertainty analyses that can be performed on large models, and compares different approaches to uncertainty analysis by explaining underlying assumptions, suitability for different model types, and advantages and disadvantages. The appendix presents a summary of integrated assessment models for climate change that explicitly account for uncertainty.     

Potential Impact of Climate Change on Residential Energy Consumption in Dhaka City

This study aimed to assess the impacts of climate change on residential energy consumption in Dhaka city of Bangladesh. The monthly electricity consumption data for the period 2011–2014 and long-term climate variables namely monthly rainfall and temperature records (1961–2010) were used in the study. An ensemble of six global circulation models (GCMs) of coupled model intercomparison project phase 5 (CMIP5) namely, BCCCSM1-1, CanESM2, MIROC5, MIROC-ESM, MIROC-ESM-CHEM, and NorESM1-M under four representative concentration pathway (RCP) scenarios were used to project future changes in rainfall and temperature. The regression models describing the relationship between historical energy consumption and climate variables were developed to project future changes in energy consumptions. The results revealed that daily energy consumption in Dhaka city increases in the range of 6.46–11.97 and 2.37–6.25 MkWh at 95% level of confidence for every increase of temperature by 1 °C and daily average rainfall by 1 mm, respectively. This study concluded that daily total residential energy demand and peak demand in Dhaka city can increase up to 5.9–15.6 and 5.1–16.7%, respectively, by the end of this century under different climate change scenarios.     

Advancing Energy Access Modelling with Geographic Information System Data

One of the main goals in pursuing sustainable development is to provide universal access to modern energy services, notably through the use of off-grid renewable energy technologies. To date, integrated assessment models (IAMs) poorly address energy access targets. In the context of research dedicated to energy scenarios and climate change mitigation in Africa, we attempt to advance the representation of energy access in one such IAM by using GIS data. In a case study for Ethiopia with the TIAM-ECN model, we demonstrate that by enriching an IAM with information derived from GIS databases, insights are obtained that better capture the dynamics of energy access developments, in comparison to conventional IAM analysis of energy technology deployment pathways. When duly accounting for the geographical spread in demography and technology costs in a developing country, we find that many people may gain access to electricity in remote areas thanks to the availability of affordable off-grid power production options that render expensive grid extensions unnecessary. This effect is not explicitly accounted for in most traditional IAMs. By the middle of the century, off-grid technologies could provide affordable electricity to 70% of the Ethiopian population, based almost entirely on renewable sources such as wind, solar and hydropower.     

A glimpse at short-term controls of evapotranspiration along the southern slopes of Kilimanjaro

Future climate characteristics of the southern Kilimanjaro region, Tanzania, are mainly determined by local land-use and global climate change. Reinforcing increasing dryness throughout the twentieth century, ongoing land transformation processes emphasize the need for a proper understanding of the regional-scale water budget and possible implications on related ecosystem functioning and services. Here, we present an analysis of scintillometer-based evapotranspiration (ET) covering seven distinct habitat types across a massive climate gradient from the colline savanna woodlands to the upper-mountain Helichrysum zone (940 to 3960 m.a.s.l.). Random forest-based mean variable importance indicates an outstanding significance of net radiation (R _net) on the observed ET across all elevation levels. Accordingly, topography and frequent cloud/fog events have a dampening effect at high elevations, whereas no such constraints affect the energy and moisture-rich submontane coffee/grassland level. By contrast, long-term moisture availability is likely to impose restrictions upon evapotranspirative net water loss in savanna, which particularly applies to the pronounced dry season. At plot scale, ET can thereby be approximated reasonably using R _net, soil heat flux, and to a lesser degree, vapor pressure deficit and rainfall as predictor variables (R ² 0.59 to 1.00). While multivariate regression based on pooled meteorological data from all plots proves itself useful for predicting hourly ET rates across a broader range of ecosystems (R ² = 0.71), additional gains in explained variance can be achieved when vegetation characteristics as seen from the NDVI are considered (R ² = 0.87). To sum up, our results indicate that valuable insights into land cover-specific ET dynamics, including underlying drivers, may be derived even from explicitly short-term measurements in an ecologically highly diverse landscape.     

Influences of Climatic Change on Some Ecological Processes of an Insect Outbreak System in Canada's Boreal Forests and the Implications for Biodiversity

Insect outbreaks are a major disturbance factor in Canadian forests. If global warming occurs, the disturbance patterns caused by insects may change substantially, especially for those insects whose distributions depend largely on climate. In addition, the likelihood of wildfire often increases after insect attack, so the unpredictability of future insect disturbance patterns adds to the general uncertainty of fire regimes. The rates of processes fundamental to energy, nutrient, and biogeochemical cycling are also affected by insect disturbance, and through these effects, potential changes in disturbance patterns indirectly influence biodiversity. A process-level perspective is advanced to describe how the major insect outbreak system in Canadian forests, that of the spruce budworm (Choristoneura fumiferana Clem. [Lepidoptera: Tortricidae]), might react to global warming. The resulting scenarios highlight the possible importance of natural selection, extreme weather, phenological relationships, complex feedbacks, historical conditions, and threshold behavior. That global warming already seems to be affecting the lifecycles of some insects points to the timeliness of this discussion. Some implications of this process-level perspective for managing the effects of global warming on biodiversity are discussed. The value of process-level understanding and high-resolution, long-term monitoring in attacking such problems is emphasized. It is argued that a species-level, preservationist approach may have unwanted side-effects, be cost-ineffective, and ecologically unsustainable.     

Industrial transformation towards sustainability of the energy system

Human induced climate change is one of the single most significant indicators that human society is not pursuing a sustainable trajectory. Managing the risks requires a major transformation of the way energy needs are met. Such a transformation includes changes in the production and consumption system and the incentive structure that shapes this system. The major driving force for transformation is the public concern about the environmental impact of the present fossil fuel based energy system. We may expect that energy producers, encouraged by governments, NGOs and consumer preferences will be responding to these concerns and expectations sooner or later. In fact a number of major international energy companies are presently adjusting their strategies to the needs and concerns of the public. A mix of measures including energy efficiency, a switch to natural gas, major investments in low carbon and renewable energy technologies and underground storage of carbon are elements of such new strategies. Consumers in a number of OECD countries have expressed their willingness to pay more for energy, provided it is green and clean. NGOs continue to put pressure on governments to deal with the climate problem. The challenge for governments is to develop an institutional framework that helps the producers and consumers to go through a transformation of the energy system. As different groups in society are likely to support different strategies, this paper suggests that a pluralistic policy approach including efficiency standards, renewable energy portfolio standards, carbon taxes, and the introduction of a system of tradable emission permits is the most promising approach for a transformation towards a low carbon energy economy. Research can support a transformation of the energy system by exploring the various transformation scenarios. Such research should take a multi-disciplinary approach, it should focus on the energy system as a whole, including production, consumption and the incentive structure that shapes the interaction between the two and it should be international in scope. This revised version was published online in July 2006 with corrections to the Cover Date.     

Consideration of climate change impacts and adaptation in EIA practice — Perspectives of actors in Austria and Germany

Current political discussions and developments indicate the importance and urgency of incorporating climate change considerations into EIA processes. The recent revision of the EU Directive 2014/52/EU on Environmental Impact Assessment (EIA) requires changes in the EIA practice of the EU member states. This paper investigates the extent to which the Environmental Impact Assessment (EIA) can contribute to an early consideration of climate change consequences in planning processes. In particular the roles of different actors in order to incorporate climate change impacts and adaptation into project planning subject to EIA at the appropriate levels are a core topic. Semi-structured expert interviews were carried out with representatives of the main infrastructure companies and institutions responsible in these sectors in Austria, which have to carry out EIA regularly. In a second step expert interviews were conducted with EIA assessors and EIA authorities in Austria and Germany, in order to examine the extent to which climate-based changes are already considered in EIA processes. This paper aims to discuss the different perspectives in the current EIA practice with regard to integrating climate change impacts as well as barriers and solutions identified by the groups of actors involved, namely project developers, environmental competent authorities and consultants (EIA assessors/practitioners). The interviews show that different groups of actors consider the topic to different degrees. Downscaling of climate change scenarios is in this context both, a critical issue with regards to availability of data and costs. Furthermore, assistance for the interpretation of relevant impacts, to be deducted from climate change scenarios, on the specific environmental issues in the area is needed. The main barriers identified by the EIA experts therefore include a lack of data as well as general uncertainty as to how far climate change should be considered in the process without reliable data but in the presence of knowledge about possible consequences at an abstract level. A joint strategy on how to cope with uncertain prognoses about main impacts on environmental issues for areas without reliable data requires a discussion and cooperation between EIA consultants and environmental authorities.     

Carrots and sticks for new technology: Abating greenhouse gas emissions in a heterogeneous and uncertain world

Many governments use technology incentives as an important component of their greenhouse gas abatement strategies. These carrots are intended to encourage the initial diffusion of new, greenhouse-gas-emissions-reducing technologies, in contrast to carbon taxes and emissions trading which provide a stick designed to reduce emissions by increasing the price of high-emitting technologies for all users. Technology incentives appear attractive, but their record in practice is mixed and economic theory suggests that in the absence of market failures, they are inefficient compared to taxes and trading. This study uses an agent-based model of technology diffusion and exploratory modeling, a new technique for decision-making under conditions of extreme uncertainty, to examine the conditions under which technology incentives should be a key building block of robust climate change policies. We find that a combined strategy of carbon taxes and technology incentives, as opposed to carbon taxes alone, is the best approach to greenhouse gas emissions reductions if the social benefits of early adoption sufficiently exceed the private benefits. Such social benefits can occur when economic actors have a wide variety of cost/performance preferences for new technologies and either new technologies have increasing returns to scale or potential adopters can reduce their uncertainty about the performance of new technologies by querying the experience of other adopters. We find that if decision-makers hold even modest expectations that such social benefits are significant or that the impacts of climate change will turn out to be serious then technology incentive programs may be a promising hedge against the threat of climate change.     

Mercury Air Emission from Coal-Fired Public Power Sector: Uncertainty and Its Monthly Distribution. Case Study from Poland

The paper presents two aspects concerned with the mercury air emission inventory from coal-fired public power and energy plants: an uncertainty analysis, using Monte Carlo simulation (Journal of the American Statistical Association, 44(247), 335–341 1949) and the monthly distributions applying the Denton-Cholette approach (Dagum & Cholette 2006). The analysis determines uncertainty about the estimates mercury air emission from 1990 to 2012 including the development of air pollution control (APC) technologies in the Polish public power and energy sector, also the monthly distributions in comparison with previously obtained results (H?awiczka 2008). The uncertainty of mercury (Hg) content in fuel is 31.6% for hard coal and 42.4% for brown coal. The confidence interval for the estimated emission changed from [kg] (16,082.2; 16,242.2) in 1990 to (10,525.9;10,671.1) in 2012. However, the Denton-Cholette approach overestimates the emissions for the warmer periods of the year, but it could, however, in our view, be applied to attain the monthly distributions.     

Environmental Objectives,Cost Efficiency,and Multivariate Stochastic Control

This paper is concerned with the cost efficiency in achieving the Swedish national air quality objectives under uncertainty. To realize an ecologically sustainable society, the parliament has approved a set of interim and long-term pollution reduction targets. However, there are considerable quantification uncertainties on the effectiveness of the proposed pollution reduction measures. In this paper, we develop a multivariate stochastic control framework to deal with the cost efficiency problem with multiple pollutants. Based on the cost and technological data collected by several national authorities, we explore the implications of alternative probabilistic constraints. It is found that a composite probabilistic constraint induces considerably lower abatement cost than separable probabilistic restrictions. The trend is reinforced by the presence of positive correlations between reductions in the multiple pollutants.

Spatial Distribution of Forest Fires and Controlling Factors in Andhra Pradesh, India Using Spot Satellite Datasets

Fires are one of the major causes of forest disturbance and destruction in several dry deciduous forests of southern India. In this study, we use remote sensing data sets in conjunction with topographic, vegetation, climate and socioeconomic factors for determining the potential causes of forest fires in Andhra Pradesh, India. Spatial patterns in fire characteristics were analyzed using SPOT satellite remote sensing datasets. We then used nineteen different metrics in concurrence with fire count datasets in a robust statistical framework to arrive at a predictive model that best explained the variation in fire counts across diverse geographical and climatic gradients. Results suggested that, of all the states in India, fires in Andhra Pradesh constituted nearly 13.53% of total fires. District wise estimates of fire counts for Andhra Pradesh suggested that, Adilabad, Cuddapah, Kurnool, Prakasham and Mehbubnagar had relatively highest number of fires compared to others. Results from statistical analysis suggested that of the nineteen parameters, population density, demand of metabolic energy (DME), compound topographic index, slope, aspect, average temperature of the warmest quarter (ATWQ) along with literacy rate explained 61.1% of total variation in fire datasets. Among these, DME and literacy rate were found to be negative predictors of forest fires. In overall, this study represents the first statewide effort that evaluated the causative factors of fire at district level using biophysical and socioeconomic datasets. Results from this study identify important biophysical and socioeconomic factors for assessing ‘forest fire danger’ in the study area. Our results also identify potential ‘hotspots’ of fire risk, where fire protection measures can be taken in advance. Further this study also demonstrate the usefulness of best-subset regression approach integrated with GIS, as an effective method to assess ‘where and when’ forest fires will most likely occur.     

Regional impact analysis of climate change on natural and managed forests in the Federal State of Brandenburg, Germany

A methodology for regional application of forest simulation models has been developed as part of an assessment of possible climate change impacts in the Federal state of Brandenburg (Germany). Here we report on the application of a forest gap model to analyse the impacts of climate change on species composition and productivity of natural and managed forests in Brandenburg using a statistical method for the development of climate scenarios. The forest model was linked to a GIS that includes soil and groundwater table maps, as well as gridded climate data with a resolution of 10 × 10 km and simulated a steady-state species composition which was classified into forest types based on the biomass distribution between species. Different climate scenarios were used to assess the sensitivity of species composition to climate change. The simulated forest distribution patterns for current climate were compared with a map of Potential Natural Vegetation (PNV) of Brandenburg.In order to analyse the possible consequences of climate change on forest management, we used forest inventory data to initialize the model with representative forest stands. Simulation experiments with two different management strategies indicated how forest management could respond to the projected impacts of climate change. The combination of regional analysis of natural forest dynamics under climate change with simulation experiments for managed forests outlines possible trends for the forest resources. The implications of the results are discussed, emphasizing the regional differences in environmental risks and the adaptation potentials of forestry in Brandenburg.     

Singular Spectrum Analytic (SSA) Decomposition and Reconstruction of Flowering: Signatures of Climatic Impacts

An empirical approach for the decomposition and reconstruction of long-term flowering records of eight eucalypt species is presented. Results from singular spectrum analysis (SSA) allow for characterisation of the dynamic and complex flowering patterns in response to temperature and rainfall throughout the year. SSA identified trend, annual, biennial and other sub-components of flowering. The ability to discriminate flowering and climate relationships is demonstrated based on SSA (cross-)correlation analysis. SSA also identified the cyclical influence of temperature and rainfall on peak flowering. For each species, there is, on average, 6 months of the annual cycle when temperature positively influences flowering and 6 months when the influence of temperature is negative. For all species, rainfall exerts a negative influence when temperature is positive. Investigation of short-term and long-term lags of climate on flowering provided best-case climatic scenarios for each species’ flowering; e.g. more intense peak flowering is likely in Eucalyptus leucoxylon when cool, wet conditions coincide with peak flowering and is further enhanced if the preceding autumn and winter were warm and dry, and the previous spring and summer cool and wet. Three clear species groupings, according to similar SSA (cross-)correlation signatures, were identified: (1) E. leucoxylon and E. tricarpa; (2) E. camaldulensis, E. melliodora and E. polyanthemos and (3) E. goniocalyx, E. microcarpa and E. macrorhyncha. Lastly, change point years for flowering based on SSA sub-components in four of the species seem to align with years of major shift in global ENSO signal (1951/1957/1958) as indicated by the extended multivariate ENSO index.     

CO2 Capture and Storage with Leakage in an Energy-Climate Model

Geological CO₂ capture and storage (CCS) is among the main near-term contenders for addressing the problem of global climate change. Even in a baseline scenario, with no comprehensive international climate policy, a moderate level of CCS technology is expected to be deployed, given the economic benefits associated with enhanced oil and gas recovery. With stringent climate change control, CCS technologies will probably be installed on an industrial scale. Geologically stored CO₂, however, may leak back to the atmosphere, which could render CCS ineffective as climate change reduction option. This article presents a long-term energy scenario study for Europe, in which we assess the significance for climate policy making of leakage of CO₂ artificially stored in underground geological formations. A detailed sensitivity analysis is performed for the CO₂ leakage rate with the bottom-up energy systems model MARKAL, enriched for this purpose with a large set of CO₂ capture technologies (in the power sector, industry, and for the production of hydrogen) and storage options (among which enhanced oil and gas recovery, enhanced coal bed methane recovery, depleted fossil fuel fields, and aquifers). Through a series of model runs, we confirm that a leakage rate of 0.1%/year seems acceptable for CCS to constitute a meaningful climate change mitigation option, whereas one of 1%/year is not. CCS is essentially no option to achieve CO₂ emission reductions when the leakage rate is as high as 1%/year, so more reductions need to be achieved through the use of renewables or nuclear power, or in sectors like industry and transport. We calculate that under strict climate control policy, the cumulative captured and geologically stored CO₂ by 2100 in the electricity sector, when the leakage rate is 0.1%/year, amounts to about 45,000 MtCO₂. Only a little over 10,000 MtCO₂ cumulative power-generation-related emissions are captured and stored underground by the end of the century when the leakage rate is 1%/year. Overall marginal CO₂ abatement costs increase from a few €/tCO₂ today to well over 150 €/tCO₂ in 2100, under an atmospheric CO₂ concentration constraint of 550 ppmv. Carbon costs in 2100 turn out to be about 40 €/tCO₂ higher when the annual leakage rate is 1%/year in comparison to when there is no CO₂ leakage. Irrespective of whether CCS deployment is affected by gradual CO₂ seepage, the annual welfare loss in Europe induced by the implementation of policies preventing “dangerous anthropogenic interference with the climate system” (under our assumption, implying a climate stabilisation target of 550 ppmv CO₂ concentration) remains below 0.5% of GDP during the entire century.

The FAIR model: A tool to analyse environmental and costs implications of regimes of future commitments

This article describes the policy decision-support tool, FAIR, to assess the environmental and abatement costs implications of international regimes for differentiation of future commitments. The model links long-term climate targets and global reduction objectives with regional emission allowances and abatement costs, accounting for the Kyoto Mechanisms used. FAIR consists of three sub-models: a simple climate model, an emission-allocation model and a cost model. The article also analyses ten different rule-based emission allocation schemes for two long-term concentration stabilisation targets for greenhouse gases. This analysis shows that evaluating regimes requires not only an assessment of the initial allocation, but also of the distribution of abatement costs and the impacts from emissions trading. The Multi-Stage approach (with a gradual increase of Parties adopting emission intensity or reductions targets) and the Triptych approach (with sectoral targets for all Parties) seem to provide the best prospects for most of the Parties when compared to the other allocation schemes analysed.     

Optimal Expansion of Water Quality Monitoring Network by Fuzzy Optimization Approach

River reaches are frequently classified with respect to various mode of water utilization depending on the quantity and quality of water resources available at different location. Monitoring of water quality in a river system must collect both temporal and spatial information for comparison with respect to the preferred situation of a water body based on different scenarios. Designing a technically sound monitoring network, however, needs to identify a suite of significant planning objectives and consider a series of inherent limitations simultaneously. It would rely on applying an advanced systems analysis technique via an integrated simulation-optimization approach to meet the ultimate goal. This article presents an optimal expansion strategy of water quality monitoring stations for fulfilling a long-term monitoring mission under an uncertain environment. The planning objectives considered in this analysis are to increase the protection degree in the proximity of the river system with higher population density, to enhance the detection capability for lower compliance areas, to promote the detection sensitivity by better deployment and installation of monitoring stations, to reflect the levels of utilization potential of water body at different locations, and to monitor the essential water quality in the upper stream areas of all water intakes. The constraint set contains the limitations of budget, equity implication, and the detection sensitivity in the water environment. A fuzzy multi-objective evaluation framework that reflects the uncertainty embedded in decision making is designed for postulating and analyzing the underlying principles of optimal expansion strategy of monitoring network. The case study being organized in South Taiwan demonstrates a set of more robust and flexible expansion alternatives in terms of spatial priority. Such an approach uniquely indicates the preference order of each candidate site to be expanded step-wise whenever the budget limitation is sensitive in the government agencies.