An Oligopoly Game of CDR Strategy Deployment in a Steady-State Net-Zero Emission Climate Regime

In this paper, we propose a simple oligopoly game model to represent the interactions between coalitions of countries in deploying carbon dioxide removal (CDR) strategies in a steady-state net-zero emission climate regime that could take place by the end of the twenty-first century. The emission quotas and CDR activities obtained in the solution of this steady-state model could then be used as a target for end-of-period conditions in a dynamic integrated assessment analysis studying the transition to 2100. More precisely, we analyze a steady-state situation where m coalitions exist and behave as m players in a game of supplying emission rights on an international emission trading system. The quotas supplied by a coalition must correspond to the amount of CO₂ captured through CDR activities in the corresponding world region. We use an extension of the computable general equilibrium model GEMINI-E3 to calibrate the payoff functions and compute an equilibrium solution in the noncooperative game.

     

A Satisficing Framework for Environmental Policy Under Model Uncertainty

We propose a novel framework for the economic assessment of environmental policy. Our main point of departure from existing work is the adoption of a satisficing, as opposed to optimizing, modeling approach. Along these lines, we place primary emphasis on the extent to which different policies meet a set of goals at a specific future date instead of their performance vis-a-vis some intertemporal objective function. Consistent to the nature of environmental policymaking, our model takes explicit account of model uncertainty. To this end, the decision criterion we propose is an analog of the well-known success-probability criterion adapted to settings characterized by model uncertainty. We apply our criterion to the climate-change context and the probability distributions constructed by Drouet et al. (2015) linking carbon budgets to future consumption. Insights from computational geometry facilitate computations considerably and allow for the efficient application of the model in high-dimensional settings.

     

A General Equilibrium Assessment of Climate Change Impacts on Swiss Winter Tourism with Adaptation

Winter alpine tourism has been repeatedly identified as one of the economic sectors most at risk from climate change in Switzerland. However, all of the costs that have been estimated so far for the Swiss tourism sector are, to some extent, misleading as they do not, or only partially, incorporate adaptation possibilities and general equilibrium effects. We attempt to fill this gap using a computable general equilibrium model that is specifically designed for the purposes of this research. Our modeling efforts first consist in creating a tourism sector with a part of it being dependent on snow. We also carefully model the snowmaking technology. Using climate change scenarios on future snow cover, we analyze their impacts on the Swiss ski industry. We find welfare effects for the Swiss economy ranging from − 23 to 113 million CHF in 2050. This range arises from the use of various assumptions concerning adaptation possibilities. We also show that geographical substitutions between international ski destinations have large positive effects for Switzerland. From a more general perspective, our results exemplify the risks of estimating the consequences of climate change based only on domestic impacts of climate change with no adaptation being implemented.

     

Combinatorial optimization of elk habitat effectiveness and timber harvest volume

Forest resource planning processes in the western United States have been placing an increasing emphasis on wildlife and fish habitat goals. With this in mind, we developed a method that incorporates a Habitat Effectiveness Index (HEI) for Roosevelt elk (Cervus elaphus roosevelti) into the objective function of a mathematical forest planning model. In addition, a commodity production goal is proposed (maximum timber production), and the habitat and commodity production goals are allowed to act as goals in a multi-objective goal programming planning problem. A heuristic programming technique (tabu search) is used to develop feasible solutions to the resulting non-linear, integer programming problem. Using a hypothetical example, we illustrate results of five scenarios, where the emphasis of the achievement of one or both goals is altered. The main contribution of this approach is the ability to measure and evaluate the trade-offs among achieving a certain level of a complex wildlife goal and achieving commodity production goals. These trade-offs are measured using a flexible model, allowing planners to formulate non-linear spatial goals as objectives of a problem, rather than forcing them to rely on posterior evaluations of the suitability of management plans to goals such as elk HEI.     

A Mechanical Model for Sieving of Large Deposited Atmospheric Particulates in Surface Soil

A mechanical model for analyzing the sieving of large particulates in soil is proposed and a procedure for assessing soil contamination by airborne hazardous particulates is discussed. The model proposes that deposition of airborne particulates in soil is the results of transport processes of particulates in surface soil. The mechanical sieving of particulates is due to (a) trapping of particulates by the constricted necks of soil pores, (b) adhesion of particulates to pore walls, and (c) suspension in soil water. The probability of particulates being sieved is determined by the cumulative frequency of pore size distribution of the soil. The proposed model indicated that more than 95% of the falling particulates were sieved in the top 5 cm of the surface soil. Furthermore, the model estimated that the unrecorded initial fall of ¹³⁷Cs associated with contaminated particulates was in the range of 2229–2310 kBqm⁻² d⁻¹ and that the unrecorded initial depositions in the 1-cm surface layer were in the range from 129 to 135 kBqkg⁻¹ at a location of about 30 km from the Fukushima nuclear power plant.

     

Socioeconomic Risks of Extreme El Niño Event-Related Road Damages in Peru

This study evaluates the socioeconomic risk that extreme El Niño event-related road damages present to Peru by combining an environmental modelling of events’ occurrences in the country with a quantitative modelling of their effects on its economy. The dynamic of occurrence of events is modelled as a stochastic process with a vector autoregressive representation based on historical climatic data, and simulated over a 10-year period with a non-parametric bootstrap procedure. The indirect consequences of events’ related road damages are addressed with a multiregional dynamic computable general equilibrium model through an increase in interregional transportation costs and, more originally, a negative externality effect on activities’ output, which is estimated beforehand using a firm database. We find that extreme El Niño events constitute a significant one-off disaster risk for the country, threatening shifts of???2.8% in GDP and?+?1.9% in poverty rates with an annual probability p?=?1.4%. We further show that they also present a longer-term risk, leading to average annual deviations from normal trend by???0.8% in GDP and?+?0.4% in poverty rate with a probability p?=?12.6% over a 10-year period. However, we finally show that Peru might reduce these socioeconomic risks associated with these non-frequent but recurrent climatic shocks in constructing more disaster-resilient road infrastructure.

     

An Indicator for the Economic Performance and Ecological Sustainability of Nations

This document proposes a new indicator to assess countries’ sustainability. The indicator synthetises measures of economic and ecological efficiency. In other words, we assess the ability of countries to use resources to produce the maximum possible amounts of goods and services while keeping production activities’ impact on the environment as low as possible. The measure of ecological efficiency is the ecological reserve/deficit (ERD), which is based on the concept of ecological footprint. The new indicator is computed using the data envelopment analysis (DEA) model, a well-known non-parametric technique that delivers measures of productive efficiency by comparing outputs to inputs used in production. We modify the standard DEA model in two ways. Firstly, we allow for negative input and output data. Secondly, to increase DEA discriminating power of countries, we compute anti-efficiency measures. This allows us to obtain a ranking of countries based on the best and worst performances. Results show that the new efficiency indicator is valid and that high ERD positively influences the ranking of countries. Introducing anti-efficiency provides more plausible results and a more accurate ranking, for example high-polluting countries like China previously economically efficient are now ranked as low efficient when sustainability is taken into account.

     

Three-dimensional Modelling of Radionuclides Dispersion in a Marine Environment with Application to the Fukushima Dai-ichi Case

In this work, we present the implementation, verification and validation of a three-dimensional model able to reproduce the propagation of \(^{137}C_{s}\) radionuclide in coastal waters and its interaction with suspended sediments, in the framework of the open-source TELEMAC-MASCARET modelling system. The validation of the model was realized by comparing numerical results with field measurements of radionuclides concentration in the Japan Sea nearby the Fukushima Dai-ichi nuclear power plant (NPP). The developed model uses as external forcing the data available immediately after or during the accident, as, e.g. weather conditions (wind, pressure, temperature) and/or the harmonic components of tides. In contrast with previous models implemented in the study area, the model presented here is limited to the coastal area near Fukushima and refined in the coastal area close to the NPP. Numerical results show that the model is able to reproduce the propagation and diffusion of the released \(^{137}C_{s}\) in the vicinity of the Fukushima Dai-ichi NPP. Consequently, we show that the numerical results obtained with a small-scale model with a simple forcing are consistent, at a coastal scale, with models which employed a general circulation model based on data assimilation techniques or variation method for hydrodynamics. Therefore, this model could be employed in an emergency situation, when the dissolved radioactivity is considered.

     

Modeling a Carbon Capture,Transport, and Storage Infrastructure for Europe

In this paper, we develop a model to analyze the economics of carbon capture, transport, and storage (CCTS) in the wake of expected rising CO₂ prices. We present a scalable mixed integer, multiperiod, welfare-optimizing network model for Europe, called CCTS-Mod. The model incorporates endogenous decisions on carbon capture, pipeline and storage investments, as well as capture, flow and injection quantities based on given costs, CO₂ prices, storage capacities, and point source emissions. Given full information about future costs of CCTS-technology, and CO₂ prices, the model determines a cost minimizing strategy on whether to purchase CO₂ certificates, or to abate the CO₂ through investments into a CCTS-chain on a site by site basis. We apply the model to analyze different scenarios for the deployment of CCTS in Europe, e.g., under high and low CO₂ prices, respectively. We find that beyond CO₂ prices of €50 per t, CCTS can contribute to the decarbonization of Europe’s industry sectors, as long as one assumes sufficient storage capacities (onshore and/or offshore). We find that CCTS is only viable for the power sector if the CO₂ certificate price exceeds €75 per t.     

On a Temporal Investigation of Hurricane Strength and Frequency

Hurricanes originating in the West Atlantic often have devastating consequences on the cities in the US east coast, both monetary and otherwise, and hence pose a source of considerable concern to several authorities. The possibility of a connection between global warming in general and an increased frequency of these strong hurricanes is well researched, but is still actively debated. The present work tries to promote the use of a smoothing statistic termed empirical recurrence rates and to advocate the use of another, termed empirical recurrence rates ratio in a bid to better understand the rich history of these storms on one hand and to make appropriate inferences on the other, so that some light can be shed on the acceptability of conjectures held by renowned climate scientists. The methods introduced are intuitive and simple to implement and should find wide applications in diverse disciplines.

     

Interval-Parameter Conditional Value-at-Risk Two-Stage Stochastic Programming Model for Management of End-of-Life Vehicles

The management of end-of-life vehicles conserves natural resources, provides economic benefits, and reduces water, air, and soil pollution. Sound management of end-of-life vehicles is vitally important worldwide thus requiring sophisticated decision-making tools for optimizing its efficiency and reducing system risk. This paper proposes an interval-parameter conditional value-at-risk two-stage stochastic programming model for management of end-of-life vehicles. A case study is conducted in order to demonstrate the usefulness of the developed model. The model is able to provide the trade-offs between the expected profit and system risk. It can effectively control risk at extremely disadvantageous availability levels of end-of-life vehicles. The formulated model can produce optimal solutions under predetermined decision-making risk preferences and confidence levels. It can simultaneously determine the optimal long-term allocation targets of end-of-life vehicles and reusable parts as well as capital investment, production planning, and logistics management decisions within a multi-period planning horizon. The proposed model can efficiently handle uncertainties expressed as interval values and probability distributions. It is able to provide valuable insights into the effects of uncertainties. Compared to the available models, the resulting solutions are far more robust.

     

Water Quality Prediction of Water Sources Based on Meteorological Factors using the CA-NARX Approach

With the increasingly serious problem of surface water environmental safety, it is of great significance to study the changing trend of reservoir water quality, and it is necessary to establish a water quality prediction and early warning system for the management and maintenance of water resources. Aiming at the problem of water quality prediction in reservoirs, a CA-NARX algorithm is designed, which combines the improved dynamic clustering algorithm with the idea of machine learning and the forward dynamic regression neural network. The improved dynamic clustering algorithm is used to classify the eutrophication degree of waterbodies according to the total phosphorus and total nitrogen content. Considering four meteorological factors, air temperature, water temperature, water surface evaporation, and rainfall, synthetically for each water quality condition, the total phosphorus and total nitrogen in the waterbody are forecasted by an improved forward NARX dynamic regression neural network. Based on this, the CA-NARX prediction algorithm can realize short period water quality prediction. Compared with the traditional support vector regression machine model, improved GA-BP neural network, and exponential smoothing method, the CA-NARX model has the least prediction error.

     

Modelling the Distribution and Quality of Sand and Gravel Resources in 3D: a Case Study in the Thames Basin,UK

Three-dimensional (3D) models are often utilised to assess the presence of sand and gravel deposits. Expanding these models to provide a better indication of the suitability of the deposit as aggregate for use in construction would be advantageous. This, however, leads to statistical challenges. To be effective, models must be able to reflect the interdependencies between different criteria (e.g. depth to deposit, thickness of deposit, ratio of mineral to waste, proportion of ‘fines’) as well as the inherent uncertainty introduced because models are derived from a limited set of boreholes in a study region. Using legacy borehole data collected during a systematic survey of sand and gravel deposits in the UK, we have developed a 3D model for a 2400 km² region close to Reading, southern England. In developing the model, we have reassessed the borehole grading data to reflect modern extraction criteria and explored the most suitable statistical modelling technique. The additive log-ratio transform and the linear model of coregionalization have been applied, techniques that have been previously used to map soil texture classes in two dimensions, to assess the quality of sand and gravel deposits in the area. The application of these statistical techniques leads to a model which can be used to generate thousands of plausible realisations of the deposit which fully reflect the extent of model uncertainty. The approach offers potential to improve regional-scale mineral planning by providing an enhanced understanding of sand and gravel deposits and the extent to which they meet current extraction criteria.

     

A New Rationale for Not Picking Low-Hanging Fruits: the Separation of Ownership and Control

Recent attempts at explaining the energy-efficiency gap rely on considerations related to organizational and behavioral/cognitive failures. In this paper, we build on the strategic delegation literature to advance a complementary explanation. We show that strategic market interaction may encourage business owners to instill a bias against energy efficiency in managerial compensation contracts. Since managers respond to financial incentives, their decisions will reflect this bias, resulting in a lack of investment.

     

WARM: a European model for energy and environmental analysis

This paper describes the structure of a newly developed econometric, imperfectly competitive, general equilibrium model for the medium term study of energy and environmental problems. The geographical coverage of the model regards twelve European countries as well as the European Union as a whole. Compared to existing quantitative E3 (economy-energy-environment) models, the WARM model is characterized by a few novel and relevant features Firstly, in contrast to multicountry interlinked models, it copes with the international dimension by integrating differences from a common European denominator within a unified and homogeneously designed framework. A panel data estimation approach is used to achieve this objective. Secondly, in contrast to the traditional market-based philosophy of many econometric models, it adopts a perspective focused upon economic agents' decisions. Thirdly, in contrast with the practice of modelling technical progress as an exogenous and deterministic phenomenon, it incorporates an explicit attempt of modelling the sources and effects of endogenous technical change. A Kalman filter latent variable approach is the methodology from which statistical information on the dynamics of technical progress can be obtained. Finally, all markets in the model are imperfectly competitive, including the labour market where the wage bargaining process is explicitly modelled and estimated. This last feature is especially important in view of the European unemployment problem.     

Generating Synthetic Rainfall on Various Timescales—Daily, Monthly and Yearly

The main objective of this paper is to present a model for generating synthetic rainfall totals on various timescales to be applicable for a variety of uses. Many large-scale ecological and water resource models require daily, monthly and yearly rainfall data as input to the model. As historical data provides only one realisation, synthetic generated rainfall totals are needed to assess the impact of rainfall variability on water resources systems (Srikanthan, In: MODSIM2005, Melbourne, Dec. 2005, pp. 1915–1921, 2005). Thus, our preferred model should simulate rainfall for yearly, monthly and daily periods. We believe that, for water supply issues, no higher resolution is needed, although higher resolution would be useful in models designed to measure the risk of local flooding. The critical factors are daily, monthly and yearly totals and daily, monthly and yearly variation. A model for generating yearly totals will be described using traditional time series methods. This model, along with a similarly constructed daily generation model by Piantadosi et al. (A New Model for Correlated Daily Rainfall, 2008), will be cascaded to start with a synthetic yearly total, then generate a synthetic sequence of monthly totals (through selection from a large number of realisations) that match the yearly total, and subsequently perform a similar operation for sequences of daily totals to match the required monthly totals. We present a new model for the generation of synthetic monthly rainfall data, which we demonstrate for Parafield in Adelaide, South Australia. The rainfall for each month of the year is modelled as a non-negative random variable from a mixed distribution with either a zero outcome or a strictly positive outcome. We use maximum likelihood to find parameters for both the probability of a zero outcome and the gamma distribution that best matches the observed probability density for the strictly positive outcomes. We describe a new model that generates correlated monthly rainfall totals using a diagonal band copula with a single parameter to generate lag-1 correlated random numbers. Our model preserves the marginal monthly distributions and, hence, also preserves the monthly and yearly means. We show that, for Parafield, the correlation between rainfall totals for successive months is not significant, and so, it is reasonable to assume independence. This is, however, not true for daily rainfall. We describe a new model that generates correlated daily rainfall totals using a diagonal band copula with a single parameter to generate lag-1 correlated random numbers. This is an extended version of a paper presented at the 17th Biennial Congress on Modelling and Simulation, Christchurch, New Zealand, December 2007.     

Protecting Sensitive Coastal Areas with Exclusion Booms during Oil Spill Events

Oil spills at sea remain a serious threat to coastal settlements and sensitive ecosystems. Although the impacts of spills are contingent upon a variety of environmental factors and the chemical composition of the oil itself, spill effects can be long lasting in the pelagic zone with broad impacts on sensitive bacterial, microbial, plant, and animal communities. Efforts to contain, deflect, protect, and mitigate the effects of oil are increasingly important, given the massive social, economic, and environmental fallout connected to large spills. The purpose of this paper is to provide geographic perspective for protecting coastal areas with exclusion booms during oil spill events. Specifically, we introduce a generalized, extendable, spatial optimization model that simultaneously minimizes spill effects on vulnerable shorelines and the total costs associated with dispatching booms. The multiobjective model is solved with a weighting method to produce a Pareto optimal curve that reveals how the costs and protection operations change under different priorities. A simulated tanker spill near Mobile Bay, AL, USA, is used as an illustrative example.

     

Synthesis,characterization, and systematic studies of a novel aluminum selective chelating resin

A procedure is detailed for the selective analysis of trace aluminum by flame atomic absorption spectrophotometer coupled with off-line column separation and preconcentration. Chelating resin was synthesized by covalent functionalization of Amberlite XAD-16 by 2-(2-hydroxyphenyl) benzoxazole. The modified resin was characterized using FT-IR spectroscopy, energy dispersive x-ray analysis, elemental analysis, thermogravimetric analysis/differential thermal analysis, and minimum energy run. The optimum sorption was at pH 9?±?0.1 with corresponding t _1/2 of only 7 min. Many competitive anions and cations studied did not interfere at all in the selective determination of Al(III), at the optimized conditions. The resin shows maximum sorption capacity of 21.58 mg g^?1 and can be regenerated up to 75 cycles without any discernible capacity loss. The Langmuir isotherm model provides the better correlation of the experimental data (r ²?=?0.999) in comparison to Freundlich isotherm model, while the Scatchard analysis revealed homogeneous binding sites in the chelating resin. Analytical figures of merit were evaluated by accuracy (standard reference materials and recovery experiment), precision (RSD <5 %), and detection limit (2.8 μg L^?1). The applicability was demonstrated by analysis of trace aluminum in biological, environmental, and food samples.     

Fuzzy Possibilistic Modeling for Closed Loop Recycling Collection Networks

Nowadays, in the competitive market, commercial companies due to their economic problems and also restrictions imposed by international organizations to comply with environmental regulations are making noticeable efforts to reduce the level of their wastes in their manufacturing systems and consequently the level of waste in consumers’ products. In harmony with this issue, one of the most effective ways which has successfully been used and proven to be economically profitable is to take products lifecycles into consideration. Consideration of products lifecycle has made supply chain practitioners to investigate the reverse logistics activities in addition to forward logistics activities. Hence, corporations, in order to reduce their cost on the one hand and boosting their efficiency on the other hand, were obligated to employ closed loop supply chain models to concurrently benefit from its economical and environmental advantages. Therefore, in this paper, a mixed nonlinear facility location–allocation model is proposed for recycling collection centers. The considered closed loop logistics model consists of multiple echelons, multiple suppliers, multiple collection centers, multiple time period and also multiple facilities. In real-life problems parameters like demand, cost, capacity, distances, and quantities of returned products are always uncertain. Therefore, in order to solve a realistic problem, foregoing parameters are considered as fuzzy in our proposed model. Subsequently, to solve fuzzy mixed nonlinear programming model, one of the most effective technique in the literature is used. Additionally, in order to demonstrate the behavior of the parameters employed in the model, a comprehensive sensitivity analysis is conducted. Computational results demonstrate that the proposed model can show favorable efficiency in solving supply chain problems.     

Investigating the Interdependence Between Non-Hydroelectric Renewable Energy,Agricultural Value Added,and Arable Land Use in Argentina

We examine the dynamic relationships between per capita carbon dioxide emissions, real gross domestic product (GDP), non-hydroelectric renewable energy (NHRE) consumption, agricultural value added (AVA), and agricultural land (AGRL) use for the case of Argentina over the period 1980–2013 by employing the autoregressive distributed lag bound approach to cointegration and Granger causality tests. The Fisher statistics of the Wald test are examined, and the existence of a long-run cointegration between variables is proved. There are long-run bidirectional causalities between all considered variables. The short-run Granger causality suggests bidirectional causality between AVA and agricultural land use, unidirectional causalities running from AGRL to NHRE and from NHRE to AVA. Long-run elasticity estimates suggest that increasing AGRL reduces carbon emissions; increasing AVA increases GDP and reduces pollution, AGRL, and NHRE; and increasing NHRE reduces AVA and AGRL. Thus, it seems that agriculture and renewable energy are substitute activities and compete for land use. We recommend that Argentina should continue to encourage agricultural production. The substitutability between agricultural and non-hydroelectric renewable energy productions, and their competition for agricultural land use, should be at least reduced or even stopped by encouraging research and development in second-generation (or even in third-generation) biofuel production and in new technologies for renewable energy and for agriculture more efficient in land use.