Identification of optimal strategies for improving eco-resilience to floods in ecologically vulnerable regions of a wetland

In this study, a mixed integer fuzzy interval-stochastic programming model was developed for supporting the improvement of eco-resilience to floods in wetlands. This method allows uncertainties that are associated with eco-resilience improvement and can be presented as both probability distributions and interval values to be incorporated within a general modeling framework. Also, capacity-expansion plans of eco-resilience can be addressed through introducing binary variables. Moreover, penalties due to ecological damages which are associated with the violation of predefined targets can be effectively incorporated within the modeling and decision process. Thus, complexities associated with flood resistance and eco-resilience planning in wetlands can be systematically reflected, highly enhancing robustness of the modeling process. The developed method was then applied to a case of eco-resilience enhancement planning in three ecologically vulnerable regions of a wetland. Interval solutions under different river flow levels and different ecological damages were generated. They could be used for generating decision alternatives and thus help decision makers identify desired eco-resilience schemes to resist floods without causing too much damages. The application indicates that the model is helpful for supporting: (a) adjustment or justification of allocation patterns of ecological flood-resisting capacities, (b) formulation of local policies regarding eco-resilience enhancement options and policy interventions, and (c) analysis of interactions among multiple administrative targets within a wetland.     

An interval joint-probabilistic programming method for solid waste management: a case study for the city of Tianjin, China

Currently, environmental protection and resources conservation continue to be challenges faced by solid-waste managers in China. These challenges are being further compounded by rapid socioeconomic devel- opment and population growth associated with increased waste generation rates and decreased waste disposal capacities. In response to these challenges, an interval joint-probabilistic mixed-integer programming （IJMP） method is developed for supporting long-term planning of waste management activities in the city of Tianjin, which is one of the largest municipalities in the northern part of China. In the IJMP, joint probabilistic constraints are introduced into an interval-parameter mixed-integer programming framework, such that uncertainties presented in terms of interval values and random variables can be reflected. Moreover, a number of violation levels for the waste-management-capacity constraints are examined, which can facilitate in-depth analyses of tradeoffs among economic objective and system-failure risk. The results indicate that reasonable solutions have been generated. They are valuable for supporting the adjustment of the city＇s existing waste-management practices and the long- term planning of the city＇s waste-management facilities.     

Strategic climate policy with offsets and incomplete abatement: Carbon taxes versus cap-and-trade

This paper provides a first analysis of a “policy bloc” of fossil fuel importers which implements an optimal climate policy, faces a (non-policy) fringe of other fuel importers, and an exporter bloc, and purchases offset from the fringe. We compare a carbon tax and a cap-and-trade scheme for the policy bloc, in either case accompanied by an efficient offset mechanism for reducing emissions in the fringe. The policy bloc is shown to prefer a tax over a cap, since only a tax reduces the fuel export price and by more when the policy bloc is larger. Offsets are also more favorable to the policy bloc under a tax than under a cap. The optimal offset price under a carbon tax is below the tax rate, while under a cap and free quota trading the offset price must equal the quota price. The domestic carbon and offset prices are both higher under a tax than under a cap when the policy bloc is small. When the policy bloc is larger, the offset price can be higher under a cap. Fringe countries gain by mitigation in the policy bloc, more under a carbon tax since the fuel import price is lower.     

Green tax reform,endogenous innovation and the growth dividend

We study theoretically and numerically the effects of an environmental tax reform using endogenous growth theory. In the theoretical segment, mobile labor between manufacturing and R&D activities, and elasticity of substitution between labor and energy in manufacturing lower than unity allow for a growth dividend, even if we consider preexisting tax distortions. The scope for innovation is reduced when we consider direct financial investment in the lab, or elastic labor supply. We then apply the core theoretical model to a real growing economy and find that a boost in long-run economic growth following such a carbon policy is a possible outcome. Redistribution of additional carbon tax revenue by lowering capital taxation performs best in terms of effciency measured by aggregate welfare. In terms of equity among social segments the progressive character of lump-sum redistribution fails when we consider very high emissions reduction targets.     

Environmental policy à la carte: Letting firms choose their regulation

Under uncertainty, the optimal choice between price and quantity instruments depends on the technology of the regulated firms, which is often private information. We consider an environmental policy that delegates the prices-versus-quantities decision to the firms by offering them the choice between an emissions tax and permit trading. Such an approach is currently used in Swiss climate policy. We provide a detailed characterization of the optimal policy and show that this approach reduces expected social costs compared to a pure tax or permit-trading regime. We demonstrate that an optimal allocation of firms to instruments can be achieved despite substantial informational constraints, and that all firms gain from the introduction of the instrument choice compared to optimally designed single-instrument policies. Furthermore, we discuss the conditions under which this approach is likely to be preferable to a hybrid regulation.     

An integrated optimization and simulation approach for air pollution control under uncertainty in open-pit metal mine

Air Pollution Control model is developed for open-pit metal mines. Model will aid decision makers to select a cost-effective solution. Open-pit metal mines contribute toward air pollution and without effective control techniques manifests the risk of violation of environmental guidelines. This paper establishes a stochastic approach to conceptualize the air pollution control model to attain a sustainable solution. The model is formulated for decision makers to select the least costly treatment method using linear programming with a defined objective function and multi-constraints. Furthermore, an integrated fuzzy based risk assessment approach is applied to examine uncertainties and evaluate an ambient air quality systematically. The applicability of the optimized model is explored through an open-pit metal mine case study, in North America. This method also incorporates the meteorological data as input to accommodate the local conditions. The uncertainties in the inputs, and predicted concentration are accomplished by probabilistic analysis using Monte Carlo simulation method. The output results are obtained to select the cost-effective pollution control technologies for PM_2.5, PM₁₀, NO_x, SO₂ and greenhouse gases. The risk level is divided into three types (loose, medium and strict) using a triangular fuzzy membership approach based on different environmental guidelines. Fuzzy logic is then used to identify environmental risk through stochastic simulated cumulative distribution functions of pollutant concentration. Thus, an integrated modeling approach can be used as a decision tool for decision makers to select the cost-effective technology to control air pollution.     

An integrated model for structure optimization and technology screening of urban wastewater systems

The conventional approach to wastewater system design and planning considers each component separately and does not provide the optimum performance of the entire system. However, the growing concern for environmental protection, economic efficiency, and sustainability of urban wastewater systems requires an integrated modeling of subsystems and a synthetic evaluation of multiple objectives. In this study, a multi-objective optimization model of an integrated urban wastewater system was developed. The model encompasses subsystems, such as a sewer system, stormwater management, municipal wastewater treatment, and a wastewater reclamation system. The non-dominated sorting genetic algorithm (NSGA-II) was used to generate a range of system design possibilities to optimize conflicting environmental and economic objectives. Information from a knowledge base, which included rules for generating treatment trains as well as the performance characteristics of commonly used water pollution control measures, was utilized. The trade-off relationships between the objectives, total water pollution loads to the environment, and life cycle costs (which consist of investment as well as operation and maintenance costs), can be illustrated using Pareto charts. The developed model can be used to assist decision makers in the preliminary planning of system structure. A benchmark city was constructed to illustrate the methods of multi-objective controls, highlight cost-effective water pollution control measures, and identify the main pressures on urban water environment.     

Is Chinese trade policy motivated by environmental concerns?

This paper analyses whether China's export VAT rebates and export taxes are driven by environmental concerns. Since China struggles to enforce environmental regulation, trade policy can be used as a second-best environmental policy. In a general equilibrium model it is possible to show that the second-best export tax increases in a product's pollution intensity. The empirical analysis investigates whether the export tax equivalent of partial VAT rebates and export taxes are higher for products which are more pollution intensive along several dimensions. The results indicate that the VAT rebate rates are set in a way that discourages exports of water pollution intensive, SO₂ intensive and energy intensive products from 2007 on. Moreover, the conservation of natural resources such as minerals, metals, wood products and precious stones seems to be a key determinant of China's export VAT rebate rates. There is little evidence that export taxes are motivated by environmental concerns.     

应用CGE模型量化分析中国实施能源环境税政策的可行性

应用一个可计算一般均衡(CGE)模型对中国若干工业行业按照差别税率对所投入的煤炭和油气产品征收从价能源环境税的情况进行了量化模拟,预测了该项政策在中国实施的可行性;并在量化模拟和分析之后得出如下结论:即征收能源环境税有助于促进工业部门,尤其是能源密集型行业,节能、降耗、改善能源消费结构和削减SO2 和CO2 污染排放,但该项政策的实施并不能够从根本上促进能源利用效率的提高,反而易对宏观经济造成负面影响,因此在中国目前现阶段不宜作为主要能源环境政策备选方案     

Re-evaluating the role of energy efficiency standards: A behavioral economics approach

The economic models that prescribe Pigovian taxation as the first-best means of reducing energy-related externalities are typically based on the neoclassical model of rational consumer choice. Yet, consumer behavior in markets for energy-using durables is generally thought to be far from efficient, giving rise to the concept of the “energy-efficiency gap.” This paper presents a welfare analysis of energy policies that is based on a behavioral model of temptation and self-control, introduced by Gul and Pesendorfer 23 and 24. We find that, in the presence of temptation, (i) Pigovian taxes alone do not yield a first-best outcome, (ii) when viewed as substitutes, energy efficiency standards can dominate Pigovian taxes, and (iii) a policy combining standards with a Pigovian tax can yield higher social welfare than a Pigovian tax alone, implying that the two instruments should be viewed as complements rather than substitutes.     

Using Strategic Foresight to Assess Conservation Opportunity

The nature of conservation challenges can foster a reactive, rather than proactive approach to decision making. Failure to anticipate problems before they escalate results in the need for more costly and time‐consuming solutions. Proactive conservation requires forward‐looking approaches to decision making that consider possible futures without being overly constrained by the past. Strategic foresight provides a structured process for considering the most desirable future and for mapping the most efficient and effective approaches to promoting that future with tools that facilitate creative thinking. The process involves 6 steps: setting the scope, collecting inputs, analyzing signals, interpreting the information, determining how to act, and implementing the outcomes. Strategic foresight is ideal for seeking, recognizing, and realizing conservation opportunities because it explicitly encourages a broad‐minded, forward‐looking perspective on an issue. Despite its potential value, the foresight process is rarely used to address conservation issues, and previous attempts have generally failed to influence policy. We present the strategic foresight process as it can be used for proactive conservation planning, describing some of the key tools in the foresight tool kit and how they can be used to identify and exploit different types of conservation opportunities. Scanning is an important tool for collecting and organizing diverse streams of information and can be used to recognize new opportunities and those that could be created. Scenario planning explores how current trends, drivers of change, and key uncertainties might influence the future and can be used to identify barriers to opportunities. Backcasting is used to map out a path to a goal and can determine how to remove barriers to opportunities. We highlight how the foresight process was used to identify conservation opportunities during the development of a strategic plan to address climate change in New York State. The plan identified solutions that should be effective across a range of possible futures. Illustrating the application of strategic foresight to identify conservation opportunities should provide the impetus for decision makers to explore strategic foresight as a way to support more proactive conservation policy, planning, and management.     

Do renewable energy policies reduce carbon emissions? On caps and inter-industry leakage

In a parsimonious two-sector general equilibrium model, we challenge the widely-held tenet that within a cap-and-trade system renewable energy policies have no effect on carbon emissions. If the cap does not capture all sectors, we demonstrate that variations of a renewable energy subsidy change aggregate carbon emissions through an inter-industry leakage effect. We decompose this effect into intuitively intelligible components that depend in natural ways on measurable elasticity parameters. Raising the subsidy always reduces emissions if funded by a lump-sum tax, reinforcing recent findings that tightening environmental regulation can cause negative leakage. However, if the subsidy is funded by a levy on electricity, it can increase emissions. These results provide a valuable basis for an informed design of renewable energy policies and an accurate assessment of their effectiveness. We highlight how a state-of-the-art statistic used by governments to gauge such effectiveness, “virtual emission reductions”, is biased, because inter-industrial leakage effects are not captured.     

An inexact-stochastic with recourse model for developing regional economic-ecological sustainability under uncertainty

Effective planning of resources management is important for facilitating socio-economic development and eco-environmental sustainability. Such a planning effort is complicated with a variety of uncertain, dynamic and nonlinear factors as well as their interactions. In this study, an inexact-stochastic quadratic programming with recourse (ISQP-R) method is developed for reflecting dynamics of system uncertainties based on a complete set of scenarios as well as tackling nonlinearities in the objective function to reflect the effects of marginal utility on system benefits and costs. Moreover, since penalties are exercised with recourse against any infeasibility, the ISQP-R can support the analysis of various policy scenarios that are associated with different levels of economic consequences when the promised targets are violated. The developed method is applied to a case study of planning resources management and developing regional ecological sustainability. The results have been generated and are helpful for decision makers in not only identifying desired resources-allocation strategies but also gaining insight into the tradeoff between economic objective and eco-environment violation risk.     

Derived decision rules for pollution control in a general equilibrium space economy

This paper attempts to narrow the gap between economic theory and policy inthe field of environmental pollution control by expanding the traditional general equilibrium model to include the kind of spatial detail that is important for describing pollution. This model is then used to derive theorems which provide the basis for the development of spatially differentiated, tax-based decision rules. In spite of the fact that these rules require no information on either damage costs or control costs, they maintain many of the desirable properties of the more conventional informationally intensive tax policies.     

Optimal policy instruments for externality-producing durable goods under present bias

When consumers exhibit present bias, the standard solution to market failures caused by externalities—Pigouvian pricing—is suboptimal. I investigate policies aimed at externalities for present-biased consumers. Optimal policy includes an instrument to correct the externality and an instrument to correct the present bias. Either instrument can be an incentive-based policy (e.g. a tax on fuel economy) or a command-and-control policy (e.g. a fuel economy mandate). Under consumer heterogeneity, a command-and-control policy may dominate an incentive-based policy. Calibrated to the US automobile market, simulation results suggest that the second-best gasoline tax is 3–30% higher than marginal external damages. The optimal price policy includes a gasoline tax set about equal to marginal external damages and a fuel economy tax that increases the price of an average non-hybrid car by about $550–$2200 relative to the price of an average hybrid car.     

A fuzzy goal programming with interval target model and its application to the decision problem of renewable energy planning

Environmental and Ecological Statistics - Optimizing sustainable renewable energy portfolios is one of the most complicated decision making problems in energy policy planning. This process involves...     

Effects of Carbon Taxes in an Economy with Prior Tax Distortions: An Intertemporal General Equilibrium Analysis

This paper analyzes the costs of carbon taxes in a model that recognizes interactions between this tax and pre-existing taxes. First we examine the extent to which costs of a U.S. carbon tax are reduced when its revenues finance cuts in income taxes. Such use of revenues significantly reduces, but does not eliminate, the overall policy costs. The positive overall costs reflect the carbon tax′s focus on intermediate inputs and its relatively narrow base in comparison with income taxes. We also examine the sensitivity of the carbon tax′s costs to the level of pre-existing taxes. For any given use of revenues, welfare costs rise significantly with pre-existing tax rates, indicating that models disregarding pre-existing taxes may substantially understate the costs of new environmental tax initiatives.     

Strategic environmental policy; eco-dumping or a green strategy?

The Porter hypothesis claims that a strong environmental policy best serves the interests of a nation's export industry. While this hypothesis seems to be based on some form of bounded rationality, this paper argues that governments may have good reasons for setting an especially strong environmental policy even though firms are fully rational. If the available abatement technology turns the environment into an “inferior input”, competitiveness is spurred by a strong environmental policy. The government should take advantage of this, and set an especially strict emission quota or an especially high emission tax. The findings in the paper also has consequences for the desirability of international cooperation with respect to national environmental policy. If a strict environmental policy spurs competitiveness, the environment is better protected without cooperation.     

Emissions cap or emissions tax? A multi-sector business cycle analysis

We develop a multi-sector business cycle model to analyze stochastic implications of reducing CO₂ emissions with carbon permits or with carbon taxes in the presence of multiple sources of macroeconomic uncertainty. The model is calibrated to reflect the U.S. experience. As in previous studies, using a single-sector version of our model, we find that the cap regime generates lower volatility of real variables than the tax regime, but the latter may be preferable from the welfare perspective. Still, our multi-sector analysis points to the importance of the origin of the shocks in the ranking of the two instruments and to the desirability of going beyond a single-sector analysis in evaluating their merits. We find no significant difference between the cap and the tax regimes when shocks come from non-energy sectors. In contrast, the cap has lower volatility but higher welfare costs than the tax for the shocks to energy production.     

A Comparison of Direct and Environmental Domain Approaches to Planning Reservation of Forest Higher Plant Communities and Species in Tasmania

The planning of representative systems of nature conservation reserves can be based on a wide variety of criteria. The ready availability of data on the physical attributes of the environment, and the patchiness of biological data, have made reservation planning based on environmental classifications an attractive option for decision makers. We developed an environmental classification based on ecologically relevant variables and used it to plan a forest reserve system for Tasmania. We then used biological distributional data and the same targets and procedures to choose a forest reserve system. The analyses based on the environmental classification selected the same areas as equivalent analyses based on biological distributional data to a greater degree than could be expected by chance. Many rare species and communities missed selection by environmental classificatory units (environmental domains), however, and the proportions of ranges of selected taxa varied widely. Conversely, environmental domains were missed by a reservation strategy based only on biological data. These domains might reflect some gaps in the biological data. A reservation planning approach based on both biological data and domains may produce better results than either used in isolation.