Modeling water resources management at the basin level: review and future directions

Water quality / Water resources development / Agricultural production / River basin development / Mathematical models / Simulation models / Water allocation / Policy / Economic aspects / Hydrology / Reservoir operation / Groundwater management / Drainage / Conjunctive use / Surface water / GIS / Decision support systems / Optimization methods / Water supply

The new keynesian approach to dynamic general equilibrium modeling: models, methods, and macroeconomic policy evaluation

This chapter aims to provide a hands-on approach to New Keynesian models and their uses for macroeconomic policy analysis. It starts by reviewing the origins of the New Keynesian approach, the key model ingredients and representative models. Building blocks of current-generation dynamic stochastic general equilibrium (DSGE) models are discussed in detail. These models address the famous Lucas critique by deriving behavioral equations systematically from the optimizing and forward-looking decision-making of households and firms subject to well-defined constraints. State-of-the-art methods for solving and estimating such models are reviewed and presented in examples. The chapter goes beyond the mere presentation of the most popular benchmark model by providing a framework for model comparison along with a database that includes a wide variety of macroeconomic models. Thus, it offers a convenient approach for comparing new models to available benchmarks and for investigating whether particular policy recommendations are robust to model uncertainty. Such robustness analysis is illustrated by evaluating the performance of simple monetary policy rules across a range of recently-estimated models including some with financial market imperfections and by reviewing recent comparative findings regarding the magnitude of government spending multipliers. The chapter concludes with a discussion of important objectives for on-going and future research using the New Keynesian framework

Modeling for insights not numbers: The long-term low-carbon transformation

Limiting global warming to prevent dangerous climate change requires drastically reducing global greenhouse gases emissions and a transformation towards a low-carbon society. Existing energy- and climate-economic modeling approaches that are informing policy and decision makers in shaping the future net-zero emissions society are increasingly seen with skepticism regarding their ability to forecast the long-term evolution of highly complex, nonlinear social-ecological systems. We present a structured review of state-of-the-art modeling approaches, focusing on their ability and limitations to develop and assess pathways towards a low-carbon society. We find that existing methodological approaches have some fundamental deficiencies that limit their potential to understand the subtleties of long-term low-carbon transformation processes. We suggest that a useful methodological framework has to move beyond current state of the art techniques and has to simultaneously fulfill the following requirements: (1) representation of an inherent dynamic analysis, describing and investigating explicitly the path between different states of system variables, (2) specification of details in the energy cascade, in particular the central role of functionalities and services that are provided by the interaction of energy flows and corresponding stock variables, (3) reliance on a clear distinction between structures of the sociotechnical energy system and socioeconomic mechanisms to develop it and (4) ability to evaluate pathways along societal criteria. To that end we propose the development of a versatile multi-purpose integrated modeling framework, building on the specific strengths of the various modeling approaches available while at the same time omitting their weaknesses. This paper identifies respective strengths and weaknesses to guide such development

Public evaluation of large projects : variational inequialities, bilevel programming and complementarity. A survey

Large projects evaluation rises well known difficulties because -by definition- they modify the current price system; their public evaluation presents additional difficulties because they modify too existing shadow prices without the project. This paper analyzes -first- the basic methodologies applied until late 80s., based on the integration of projects in optimization models or, alternatively, based on iterative procedures with information exchange between two organizational levels. New methodologies applied afterwards are based on variational inequalities, bilevel programming and linear or nonlinear complementarity. Their foundations and different applications related with project evaluation are explored. As a matter of fact, these new tools are closely related among them and can treat more complex cases involving -for example- the reaction of agents to policies or the existence of multiple agents in an environment characterized by common functions representing demands or constraints on polluting emissions

Efficiency Gains from "What"-Flexibility in Climate Policy: An Integrated CGE Assessment

We investigate the importance of ?what?-flexibility on top of ?where?- and ?when?-flexibility for alternative emission control schemes that prescribe long-term temperature targets and eventually impose additional constraints on the rate of temperature change. We find that ?what?-flexibility substantially reduces the compliance costs under alternative emission control schemes. When comparing policies that simply involve long-term temperature targets against more stringent strategies that include additional constraints on the rate of temperature increase, it turns out that the latter involve huge additional costs. These costs may be interpreted as additional insurance payments if damages should not only dependent on absolute temperature change but also on the rate of temperature change. --Climate policy,Integrated Assessment,What-flexibility

Network-constrained models of liberalized electricity markets: the devil is in the details

Numerical models for electricity markets are frequently used to inform and support decisions. How robust are the results? Three research groups used the same, realistic data set for generators, demand and transmission network as input for their numerical models. The results coincide when predicting competitive market results. In the strategic case in which large generators can exercise market power, the predicted prices differed significantly. The results are highly sensitive to assumptions about market design, timing of the market and assumptions about constraints on the rationality of generators. Given the same assumptions the results coincide. We provide a checklist for users to understand the implications of different modelling assumptions